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Title: 2311.01767v2.pdf
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URL Source:
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Published Time: Wed, 08 Nov 2023 02:41:27 GMT
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Number of Pages: 17
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Markdown Content:
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# PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion
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Yiduo Guo 1∗
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, Zekai Zhang 1∗
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, Yaobo Liang 2, Dongyan Zhao 1, Nan Duan 21Peking University
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> 2
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Microsoft Research Asia
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yiduo@stu.pku.edu.cn,justinzzk@stu.pku.edu.cn,yaobo.liang@microsoft.com zhaody@pku.edu.cn,nanduan@microsoft.com
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Abstract
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Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural lan-guage tasks and their ability to translate instruc-tions into tool APIs. However, the evaluation of LLMs utilizing complex tools to finish multi-turn, multi-modal instructions in a complex multi-modal environment has not been inves-tigated. To address this gap, we introduce the PowerPoint Task Completion (PPTC) bench-mark to assess LLMs’ ability to create and edit PPT files based on user instructions. It con-tains 279 multi-turn sessions covering diverse topics and hundreds of instructions involving multi-modal operations. We also propose the PPTX-Match Evaluation System that evaluates if LLMs finish the instruction based on the pre-diction file rather than the label API sequence, thus it supports various LLM-generated API sequences. We measure 3 closed LLMs and 6 open-source LLMs. The results show that GPT-4 outperforms other LLMs with 75.1% accuracy in single-turn dialogue testing but faces challenges in completing entire sessions, achieving just 6% session accuracy. We find three main error causes in our benchmark: er-ror accumulation in the multi-turn session, long PPT template processing, and multi-modality perception. These pose great challenges for fu-ture LLM and agent systems. We release the data, code, and evaluation system of PPTC at
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1 Introduction
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Recent evaluation works for Large Language Mod-els (e.g. ChatGPT and GPT-4 (OpenAI, 2023)) fo-cus on their zero-shot/few-shot abilities on basic natural language tasks (Jiao et al., 2023; Zhong et al., 2023; Wang et al., 2023b; Qin et al., 2023a) and their tool-use ability to generate APIs for solv-ing user instructions, such as basic APIs like a calculator in tool transformer (Schick et al., 2023),
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> *Equal contribution
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Figure 1: Within our benchmark, we simulate this multi-turn dialogue scenario between humans and LLMs to evaluate LLMs’ PPT task completion performance.
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RapidAPIs in ToolLLM (Qin et al., 2023c), and hugggingface APIs in Gorilla (Patil et al., 2023). However, these tool-use works emphasize the trans-lation of natural language instructions into APIs and ignore the challenge of using APIs in the ob-servation of complex multi-modal environments to finish user instructions. Also, their evaluation approach focuses on comparing the generated APIs with the label API sequence, assuming there’s only one unique solution. This approach becomes im-practicable in situations with multiple/unlimited correct solutions. To address these challenges, we introduce Power-Point Task Completion (PPTC), a benchmark that measures LLMs’ performance in creating and editing PPT file tasks based on user instructions. We choose PowerPoint as it includes various elements like textbox, table, and image and supports a wider range of APIs than Word and Ex-cel. Our benchmark has three distinctive features
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> arXiv:2311.01767v2 [cs.CL] 7 Nov 2023 Figure 2: We illustrate how LLMs complete one turn in a session. (A) To prompt the LLM, we provide it with the current instruction, previous instructions (dialogue history), PPT file content, and the API reference file. ’PPT reader’ is a function that transforms the PPT file into the text-based format as the PPT file content. (B) The LLM then generates the API sequence and executes it to obtain the prediction PPT file. (C) We evaluate attributes and position relations in the prediction file.
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from other task completion benchmarks: (1) Multi-turn dialogue with varying difficulty . Our PPTC benchmark simulates the multi-turn dialogue ses-sion between the user and the LLM (see Figure 1) and contains 279 multi-turn sessions. Each multi-turn session in our benchmark includes 2 to 17 turns. Each turn consists of a user instruction that describes the user’s needs, a feasible solution that provides the correct solution, and the resulting label output file. Some turns can be easily addressed us-ing a single API, while over half of the instructions require multiple APIs for completion. We provide the LLM with a reference API file that contains all feasible APIs for selection. (2) Multi-modality .Finishing the instruction of our benchmark requires understanding the multi-modal PPT file content and using multi-modal API operations (e.g., PPTC has 268 image operation-related instructions). (3)
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Evaluation based on the final status : We propose the PPTX-Match Evaluation system to evaluate the LLM’s outcome. To identify if the LLM completes the instruction, it checks the PPT file produced by executing the LLM-generated APIs rather than the LLM-generated APIs, thus all API sequences that lead to the correct final status are acceptable. To finish the instruction, we use the current in-struction, past turns’ instructions (dialogue history), the PPT file content (specific environment informa-tion), and the reference API file as the input to prompt the LLM to generate an API sequence as the solution (See Figure 2 (A)). Then we use the API executor to execute the API sequence and re-turn the user the resulting PPT file (See Figure 2 (B)). We name the resulting PPT file as the predic-tion file. In the evaluation step (See Figure 2 (C)), the PPTX-Match Evaluation system first uses the Python-PPTX library to extract all attributes from the prediction PPT file and the label output file. Then it uses the position relation checker to check if objects’ positions conform to the label relation and the attribute content checker to check if the at-tribute’s content is matched with the corresponding label attribute’s content. The LLM correctly com-pletes the current turn’s instruction if all attributes of the file pass these tests. Evaluation metrics in-clude turn-based accuracy which is the ratio of correctly completed turns to the total number of turns and session-based accuracy which is the ra-tio of correctly completed sessions to the overall session count. We measure the performance of three closed-source LLMs (GPT-4, ChatGPT, and Davince-003) and six open-source LLMs (e.g., LLaMa-2) in our benchmark. We further test planning (e.g., CoT (Wei et al., 2022)) and content selection al-gorithms’ performance based on GPT-4. Experi-ment results show that GPT-4 is the strongest LLM among all LLMs but still encounters challenges when completing entire multi-turn sessions. For ex-ample, although GPT-4 achieves 75.1% turn-based accuracy in the creating new PPT file task, it only achieves 22.7% session-based accuracy as errors made in previous turns. GPT-4 and other LLMs also struggle to process long PPT templates (com-plex file environment). For example, GPT-4 only achieves 38.1% turn-based accuracy in the edit-ing task. We further find that GPT-4 struggles to finish instructions involving non-text modality op-erations, especially for position-related operations, such as ’Put object A on the top of the slide’. It only achieves 24% accuracy in these instructions. In summary, this paper has the following contri-butions: (1) We propose the PowerPoint Task Completion benchmark to measure LLM’s task completion per-formance within the PowerPoint official software. This benchmark contains 279 multi-turn sessions with hundreds of multi-modal instructions in the complex multi-modal environment. (2) We propose the PPTX-evaluation system to automatically measure LLMs’ performance in our benchmark. We test 3 closed-source LLMs and 6 open-source LLMs and find that GPT-4 is the strongest LLM among all LLMs. (3) We further analyze LLMs in our benchmarks and find three key error factors: error accumulation in the session, long PPT template processing, and multi-modality perception. These findings pose significant challenges for future LLMs and LLM-based systems.
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2 PPTC Benchmark
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In this section, we introduce our Power-Point Task Completion (PPTC) benchmark, including the overview of our benchmark, its collection and validation process, and the PPTX-Match Evalua-tion System for evaluation. We further analyze the statistics information of our benchmark.
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2.1 Benchmark Overview Benchmark components Our benchmark focuses on two basic tasks within PowerPoint: creating the new PPT file and editing the existing long PPT template for measuring long PPT Content under-standing. We have gathered 229 multi-turn dia-logue sessions for creating the new PPT file and 50 sessions for editing existing templates. Each multi-turn session includes 2 to 17 turns. Each turn comprises three parts: (1) the user instruction (2) the label output file as the ground truth (3) one feasi-ble API sequence for finishing the instruction. Our benchmark also contains an API reference file that includes 49 feasible APIs for various operations and can complete all instructions in our benchmark. For each API, we describe its functionality and arguments and provide usage guidelines. For com-plex APIs, we also offer example cases. We list the details of all APIs in Appendix A.
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Task description To complete the instruction in one turn, in general, the AI assistant must compre-hend the user’s current and prior instructions for context. It should also analyze the content of the PPT file to identify relevant objects mentioned in the instruction. Additionally, it needs to select ap-propriate APIs from a reference API file to achieve the user’s goals. So we use these as the input of the AI assistant and it should output an API sequence as the solution. Then, it executes this API sequence and provides the user with the resulting PPT file as its response (See the whole process in Figure 2).
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Addressing LLM limitations in our bench-mark Compared to the general AI assistant, LLMs still have two limitations for completing the task in our benchmarks: (1) LLMs can not directly pro-cess the PPT file. So we provide a PPT reader function that extracts all shapes and their informa-tion from the PPT file and transforms them into the text format as the PPT file content. Then LLMs can understand and process the PPT file content. (2) LLMs cannot directly use PPT software through a keyboard and mouse. Therefore, we have defined PPT APIs based on the operational logic within the PPT software. and provide an implementation for these APIs in Python that can swiftly gener-ate PPT files. In future work, it may be possible to explore the use of large multimodal models to understand on-screen content and implement APIs using a keyboard and mouse.
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2.2 Benchmark Collection Design Principles We follow these principles to design our benchmark: (1) Multi-turn instructions: One session in our benchmark should contain multi-turn instructions to finish the user’s complex need. (2) Instructions of varying difficulty: Some instruc-tions can be achieved with a single API, while others necessitate a sequence of APIs for success-ful completion. (3) Diverse multimodal operations: User instructions should cover a wide range of op-erations on PPT, such as text-related, image-related, and position-related APIs. (4) Topic Consistency: The dialogue in a session should center around the session topic. Each user instruction in a session aligns closely with the previous instructions (the context), ensuring a coherent and contextually rel-evant dialogue flow. (5) Practicability First: The session topic and specific instructions should simu-late the user’s need in real world
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Benchmark Collection and Validation To col-lect user instructions, we engage 6 skilled crowd workers who craft instructions in accordance with the principles we’ve outlined. Our crowd workers comprise professional data science engineers well-versed in PowerPoint. To achieve practicability first, we request crowd workers to write instruc-tions based on their actual PowerPoint experience. On each session, the workers are asked to first find and list a practicable session topic. For the edit-ing PPT template task, the topic must based on the template file background and is practicable to the template *. To achieve multi-instructions and topic consistency, the workers write instructions step by step and make them consistent with the topic. To achieve diverse multi-operations, we ask them not to write session that only involves a single modality operation. Each worker takes on a specific role in the instructional writing work and is encouraged to write instructions in his/her own words. Workers were asked to spend at least 20 minutes on every session. We delete repeated sessions and short ses-sions that have no more than 50 tokens. Then we ask the seventh worker to write the
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> *We collect 50 PPT templates from the SlidesCarni-val website ( ). SlidesCarnival is a free and open-source PPT template web-site. Each session in the editing task has a unique template. We encourage topic diversity in our templates. We remove templates that are too short (2 ∼5 slides) and have repeated topics.
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feasible API sequence with minimal API usage for each instruction. Next, the workers create the PPT label file by using the provided API sequence. During the whole process, the principal engineer reviews and refines the instructions and API se-quences written by the above 7 workers for initial quality assurance. To ensure the data quality of this benchmark, the three authors of this paper further undertake the fol-lowing validation steps: (1) Assessing Instruction Clarity and Relevance: They examine whether the instructions are clear, contextually related to the session topic, and align with the ongoing conver-sation. (2) API Sequence Execution: The authors execute the provided API sequences to identify and rectify coding errors. (3) Goal Achievement Check: They verify if the instructions’ intended goals are successfully completed in the label files. In the event that errors are identified during this validation process, the authors promptly report them to the respective workers for revision. The three authors are computer science senior students and researchers.
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2.3 PPTX-Match Evaluation System
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We design the PPTX-Match Evaluation System to evaluate LLMs’ performance on the PPTC bench-mark. Specifically, our PPTX-Match Evaluation System first uses a Python-PPTX Content Reader Module to iterate over all shapes in the prediction PPT file produced with the LLM and the label out-put file. A shape in the PPTX library typically refers to an individual object, such as a text box or table. Then our system extracts attributes like text, style, and position of the shapes using the PPTX library. Next, we check all attributes from the prediction PPT file. For non-position attributes (e.g., text content), we first convert it and the cor-responding attribute in the label PPT file into two strings, and then we use the Exact Match method to examine if the two strings are the same. If they are different or we do not find the corresponding attribute in the label file, then we find an incorrect match. For the position attribute (e.g., location in-formation), we focus on checking if the objects in the prediction PPT file satisfy the correct position relation <A, B, REL>, where A and B are objects that should satisfy the relation REL. In the bench-mark collection process, we ask crowd workers to label the position relation that objects should sat-isfy to finish the instruction. During the evaluation Figure 3: Statistics for PPTC. a) Session turn number distribution. b) Instruction API number distribution (tokens). c) Distribution of instructions involving Chart, Table, Picture, and Position. Instructions involving ’Position’ need the system to conduct position-related operations based on the understanding of spatial information. Note that one instruction may involve multiple different modalities.
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phase, we extract the position attributes of objects A and B and use predefined functions to verify if the objects’ position attributes satisfy the position relation. If there are no incorrect matches for all non-position attributes and no rule violations for all position-related attributes, we consider the LLM has successfully completed the user instruction.
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2.4 Benchmark Statistics Analysis
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To understand the properties of PPTC, we analyze the instructions and APIs in the benchmark. Specif-ically, we explore (i) the number of turns in a ses-sion, (ii) the difficulty of the instruction in terms of the number of APIs required to finish it, and (iii) the number of multi-modality instructions. We report statistics about the PPTC benchmark in Figure 3.
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The number of turns in a session The session turn number distribution (Figure 3 (a)), measured as the number of turns in a session, shows that all sessions in our benchmark have at least two turns and almost all sessions have at least 3 turns (be-tween 3 and 13 turns for the 5th to 95th percentile, respectively). The longest session has 17 turns, which is very challenging as the errors made in previous turns can influence the completion of the current instruction.
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Diffculty varies in APIs number The number of APIs in a sequence falls between 1 and 5 for the 5th to 95th percentile (Figure 3 (b)), respec-tively, shows that our instructions’ difficulty varies from a simple sentence that can be finished by one API to a complex instruction that requires the LLM to generate multiple APIs. The longest API se-quence consists of 29 APIs. Generating long API sequences is very challenging as the LLM needs to understand sub-goals in the complex instruction, select appropriate APIs from the file, and generate APIs in a reliable order.
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Rich multi-modal instructions Our benchmark has hundreds of instructions that involve multi-modalities content (Figure 3 (c)). The "chart" modality has the fewest instructions, with 120, while the "position" modality has the most, with 292 instructions. To finish these instructions, LLMs need to employ related-modal APIs based on the understanding of multi-modal file content.
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3 Algorithms
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In this section, we introduce the algorithms we con-sidered to enhance the LLM’s performance in our benchmark. These algorithms can be categorized into two approaches: planning algorithms that help the LLM in decomposing the user instruction and solving it step by step and selection algorithms that assist the LLM in choosing important environmen-tal information or APIs.
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3.1 Planning Algorithms
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Complex user instructions often require multiple intermediate steps to complete. We mainly consider two planning algorithms:
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Zero-shot-CoT (Kojima et al., 2022) enables LLMs to autonomously generate intermediate reasoning processes for complex instruction by prompting LLMs to "Let’s think step by step".
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Tree of Thoughts (ToT) (Yao et al., 2023) en-ables LLMs to follow tree-like reasoning paths, where each tree node represents a thinking state. It leverages LLMs to generate evaluations or votes on different thoughts. 3.2 Selection Algorithms
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Combining the whole PPT file and the whole API file into the LLM’s input can result in an over-whelming amount of redundant information, such as irrelevant file content and unhelpful APIs. Filter-ing the redundant information would improve the efficiency of the LLM. In this context, we primarily focus on two algorithms for selecting the PPT file content and APIs, respectively:
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Content Selection algorithm Firstly, we extract all shapes of the PPT file by Python-PPTX. Next, we prompt the LLM to select the shapes for com-pleting the user’s instruction. We show the prompt in Figure 8, in which we add three demonstration examples to guide the LLM to do selection. In this algorithm, we replace the whole PPT file with the selected shapes when prompting the LLM to generate the API sequence.
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API Selection algorithm The API selection al-gorithm is based on the embedding similarity to select the most relevant APIs for user instructions. Specifically, we use the text embedding API to get the embeddings of all API descriptions and the cur-rent user instruction. Next, we compute the cosine similarity between the instruction embedding and each API description’s embedding and rank them based on the similarity score. In this algorithm, we replace the whole reference API file with the top
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k APIs when prompting the LLM to generate the API sequence.
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4 Experiments
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4.1 Large Language Models Selected for Evaluation
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Here, we assess different cutting-edge large lan-guage models using our benchmark. These chosen models showcase a wide array of capabilities and are highly regarded in the field. The evaluated large language models include 3 closed-source LLMs and 6 open-source LLMs: • GPT-4 (OpenAI, 2023): The latest LLM in the GPT series. GPT-4 is a cutting-edge, large-scale generative pre-trained transformer model. It offers improved performance and a wider knowledge base compared to its prede-cessors. It showcases human-level proficiency in several scenarios. • ChatGPT : ChatGPT is a conversational AI model crafted for dynamic interactions. It’s learned from extensive instruction data and fine-tuned through reinforcement learning with human feedback (RLHF). This empow-ers it to deliver responses that align with hu-man expectations, maintaining context and coherence in conversations. • Text-Davinci-003 (Brown et al., 2020): GPT-3.5 sits between GPT-3 and GPT-4, enhancing performance via additional instruction tuning. It acts as a link between these models, facil-itating comparison. We’ve chosen the Text-Davinci-003 variant from the GPT-3.5 series for our evaluation. • LLaMa-2-Chat (Touvron et al., 2023): LLaMa 2, an auto-regressive open-source lan-guage model, employs an optimized trans-former design. Chat versions utilize super-vised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to match human preferences for helpfulness and safety. • Baichuan-Chat : It is a transformer model trained on approximately 1.2 trillion tokens. It supports both Chinese and English, with a context window length of 4096. • Baichuan-2-Chat (Yang et al., 2023): It is a large-scale multilingual language model trained from scratch, on 2.6 trillion tokens. The chat version uses Supervised Fine-Tuning (SFT) and Reinforcement Learning from Hu-man Feedback (RLHF) to align with humans. • WizardLM v1.2 (Xu et al., 2023a): WizardLM v1.2 is finetuned from LLaMa 2 using super-vised instruction fine-tuning, where instruc-tions are created by rewriting the initial in-structions step by step. • Vicuna v1.5 (16k) (Chiang et al., 2023): Vi-cuna v1.5 (16k) is finetuned from LLaMa 2 us-ing supervised instruction fine-tuning and lin-ear RoPE scaling. It’s trained on about 125K conversations sourced from ShareGPT.com. • Code-LLaMa-instruct (Chiang et al., 2023): Code-LLaMa is a LLaMa-based LLM de-signed for general code completion and un-derstanding. Its instruction version further supports the chat function with users. Code LLaMa models feature a multitask training ob-jective consisting of both autoregressive and causal infilling prediction (predicting the miss-ing part of a program given a surrounding con-text).
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4.2 Experimental Setup
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In this section, we provide an overview of the ex-perimental setup utilized to assess the performance of LLMs on our PPTC benchmark.
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4.2.1 Turn-Based and Session-Based Evaluations
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We consider two performance evaluation ap-proaches in our benchmark: turn-based and session-based evaluations. For the turn-based evaluation, we measure the LLM’s ability to finish a single turn. Specifically, in this evaluation, we assume that the previous turns have been correctly finished, and we prompt the LLM to generate the API sequence to finish the current turn’s user instruction. The prompt consists of the task instruction for finishing the current user instruction, the API file contain-ing feasible APIs, the parsed PPT file content from the PPT file, and dialogue history consisting of instructions of previous turns with their feasible API sequences (see the left of Figure 4). For the session-based evaluation, we measure the LLM’s ability to finish a session containing multiple turns. For all turns in a session, we prompt the LLM to finish them sequentially. The prompt in this eval-uation has two differences: the API solutions for previous turns in dialogue history are the outputs of the LLM instead of the correct API sequences. (2) The PPT content is parsed from the PPT file obtained by executing the previous outputs of the LLM (see the right of Figure 4). That means the error made by LLMs in previous turns would influ-ence subsequent turns.
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Metrics For turn-based evaluation, we report the turn-based accuracy as the ratio of the number of successfully finished turns to the total number of turns. We also report the average token num-ber of the input of one turn and the average API number for finishing one turn as the cost measure-ment. For session-based evaluation, we report the session-based accuracy as the ratio of the number of successfully finished sessions to the total num-ber of sessions. We also report the average value of the token number of all inputs in one session and the average API number required to complete one session as the cost measurement.
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Inference prompt in PPTC
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> (Task instruction ) You are an AI assistant to help the user to operate PowerPoint and edit the contents. Give you the user instruction:<Current user instruc-tion>, you can complete it based on the following APIs and PPT file content. Current you are at page <Page id>. Please finish the user instruction with the functions you have. Don’t generate instructions be-yond what the user has instructed. Don’t guess what the user may instruct in the next step and generete API for them. Don’t use python loop to call API. You can only call API once in one line. If the user does not specify the page to be modified, you can directly start using the APIs without having to navigate to other pages. You need to generate code which can finish the user instruction. The multiple lines of code should be surrounded by <code> and </code> such as: <code> API(); API(); </code> For example, if the user instruction is "create a slide", then the answer should be: <code> create_slide(); </code> (API file )Now, you have access to alist of PowerPoint APIs with the following functions: <APIs and their descriptions> (e.g.,API(name="set_width", parameters="(width)", description="This API sets the width of the selected object.", parameter _description="It takes one parameter ’width’, the width of an object in centimeters as float.", composition _instruction="You should first choose an object before you can change the width of it.", api_desc="width of picture and shapes") ) (PPT file content ) All the PPT contents are: <Begin of PPT>
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> Turn-based: <Parsed PPT file content of the label PPT file of the previous turns> Session-based: <Parsed PPT file content of the LLM prediction file of the previous turns>
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> <End of PPT> (Dialogue history )¬User¬: Hello! ¬AI¬: Hi there! How can I help you? ¬User¬: <the first instruction> ¬AI¬:
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> Turn-based: <the correct feasible API sequence>, Session-based: <the LLM-generated API sequence>
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> ... ¬User¬: <Current user instruction>. Surrounding your answer with <code> and </code>. ¬AI¬:
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Figure 4: The inference prompt we used in both turn-based and session-based evaluation settings. In the turn-based evaluation, we assess the LLM’s performance for the current turn and assume the LLM has correctly finished previous turns. We then use feasible API se-quences of previous turns as the AI response in the dia-logue history and parse the label file of previous turns as the PPT file content. In the session-based evaluation, we evaluate the completion of the entire session and do not assume the LLM has correctly finished previous turns. We use the LLM’s generated API sequences as the response and parsed the LLM prediction file as the PPT file content. 4.3 Implementation Details
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All experiments were conducted using the respec-tive language models’ API provided by Azure Ope-nAI Service †. Azure OpenAI services offer two API types: completion and chat completion. Com-pletion API generates text from prompts, while chat completion API responds based on conversa-tion history and new input. We use the completion API for Text-Davinci-003 and the chat completion API for ChatGPT and GPT-4. We set a temperature of zero for deterministic output and a max token limit of 2048. The frequency penalty and top p are kept at their default values of zero and 1, respec-tively. We use the text-embedding-ada-002 API as the embedding API in the API selection algorithm and set k as 15. For open-source LLMs, we choose the chat version of LLaMa-2, the v1.2 version of WizardLM, and the chat version of Baichuan as our open-source LLMs. We choose the 13 billion parameters model of the three LLMs. For the zero-shot CoT method, we add the sen-tence ’Let’s think step by step’ after the dialogue history of the prompt. For the ToT method, we follow the official code to run it ‡. We run the four algorithm methods based on the GPT-4 model. If the token number of the input prompt is be-yond the token limit, we cut the PPT file content to reduce the token number of the prompt.
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4.4 Main results
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We report the results of LLMs in both turn-based and session-based evaluations in Table 1 and 2. From the results, we highlight the following key findings.
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(1) Superior Performance of GPT-4: GPT-4 consistently outperforms other closed-source and open-source LLMs in both two tasks. Impressively, GPT-4 achieves 75.1% turn-based accuracy in the creating new PPT file task, demonstrating its strong capability to finish one turn of the user instruction. GPT-4 also has a lower API cost compared to other closed-source LLMs since its precise API usage. GPT-4 incurs the highest token expense when edit-ing PPT templates. That is because its higher token limit than other LLMs allows us to input more PPT template content.
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> † en-us/products/cognitive-services/ openai-service
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> ‡ToT: tree-of-thought-llm
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(2) Code continual pre-training and further instruction finetuning can boost open-source LLMs’ performance. : Based on Table 1, it’s evi-dent that current open-source LLMs struggle to match the performance of closed-source LLMs. For example, LLaMa-2-chat only achieves 16.2% turn-based accuracy in the creating new PPT file task, which is far from the performance achieved by closed-source LLMs. We further find that code continual pretraining (Code-LLaMa) and instruc-tion fine-tuning based on LLaMa-2 (WizardLM and Vicuna) can further improve LLaMa-2 per-formance obviously. For example, Code-LLaMa improves LLaMa-2’s turn-based accuracy in the creating new PPT file task by 20.4 %. This obser-vation suggests that there’s untapped potential in open-source LLMs when it comes to our bench-mark, and this potential can be unlocked further by code pre-training and enhancing instruction follow-ing ability.
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(3) Planning and selection algorithms can im-prove LLMs’ turn-based performance From Ta-ble 2, we observe that the planning algorithms (CoT and ToT) can further improve the turn-based per-formance of GPT-4 by 1 ∼2 percent. However, we surprisingly find that the more complex ToT al-gorithm does not outperform the zero-shot CoT algorithm with a 5 ∼10 times token cost. Content and API selection algorithms can further improve the turn-based performance of GPT-4 by 1 ∼ 5 per-cent. That is because they reduce the task difficulty by filtering irrelevant PPT content/APIs in the input prompt. The API selection algorithm also reduces the average token cost by reducing the number of APIs listed in the prompt. However, for the chal-lenging session-based evaluation, these algorithms can not improve GPT-4’s performance or improve it slightly.
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4.5 Three challenges in our PPTC benchmark
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From the result Table 1 and Figure 5. we highlight the following three key challenges.
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(1) Error accumulation makes LLMs perfor-mance poor in finishing the entire multi-turn session. : The performance of all LLMs in handling sessions consisting of multiple turns is notably poor. Even GPT-4, which performs well in turn-based evaluation, achieves only a 22.7% session-based accuracy for the "creating new PPT file" task and a mere 6.0% session-based accuracy for the "editing PPT template" task. Current planning algorithms Models and Methods Creating new PPT Editing PPT template Turn-based Session-based Turn-based Session-based Accuracy Avg token Avg API Accuracy Avg token Avg API Accuracy Avg token Avg API Accuracy Avg token Avg API TD-003 72.6 2.8k 3.0 12.7 20.8k 23.9 24.4 2.9k 8.1 4.0 13.2k 26.6 ChatGPT 70.6 2.9k 3.2 12.7 20.0k 23.4 26.3 4.1k 7.9 2.0 9.2k 22.9 GPT-4 75.1 2.9k 2.9 22.7 20.8k 22.4 38.1 7.5k 7.8 6.0 24.1k 24.7 LLaMa-2 16.4 2.8k 3.9 3.4 21.6k 24.7 8.7 2.2k 7.2 0.0 9.5k 15.6 Code-LLaMa 36.8 2.8k 3.4 0.0 20.7k 32.1 18.7 3k 7.3 2.0 9.6k 22.6 WizardLM 23.9 1.3k 3.3 4.3 12.5k 22.4 10.0 1.3k 5.7 0.0 4.3k 16.5 Vicuna-v1.5 24.3 1.3k 3.9 2.2 11.0k 33.7 6.8 1.3k 6.7 0.0 4.3k 22.7 Baichuan 15.5 1.3k 9.8 0.0 10.9k 44.7 4.4 1.3k 9.6 0.0 4.3k 24.3 Baichuan-2 16.3 1.3k 9.1 3.6 11.6k 48.9 8.7 1.3k 9.2 0.0 4.2k 22.3
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Table 1: We report the results of LLMs in this table.’ TD-003’ is the Text-Davinci-003 model. We directly use the prompts in Figure 4 to prompt LLMs to generate the API sequence.
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> Models and Methods Creating new PPT file Editing PPT template Turn-based Session-based Turn-based Session-based Accuracy Avg token Avg API Accuracy Avg token Avg API Accuracy Avg token Avg API Accuracy Avg token Avg API GPT-4 75.1 2.9k 2.9 22.7 20.8k 22.4 38.1 7.5k 7.8 6.0 24.1k 24.7 GPT-4+CoT 77.0 2.9k 3.1 23.1 20.8k 22.7 40.6 7.5k 8.0 6.0 24.1k 25.2 GPT-4+ToT 76.5 20.8k 3.0 21.8 146.4k 22.6 40.6 81k 7.6 4.0 256.8k 24.0 GPT-4+Content selection 77.5 3.4k 3.0 21.8 24.5k 22.0 43.1 5.8k 8.0 4.0 18.7k 25.2 GPT-4+API selection 76.4 1.5k 2.9 18.8 10.6k 21.3 38.1 7k 8.0 10.0 22.4k 25.8
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Table 2: We report the results of GPT-4 and algorithms based on the GPT-4 model. ’CoT’ and ’ToT’ are the chain of thought and tree of thought algorithms.
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usually fail to improve session-based accuracy. In some cases, they can even make the performance worse. The session-based evaluation is challeng-ing since errors made in previous turns make the LLM fail to finish the session and also influence the completion of the current turn. Also, we need more advanced planning algorithms to complete the multi-turn session.
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(2) LLMs perform badly in processing long PPT template: Current LLMs’ performance in the editing PPT temples task is pretty poor. For exam-ple, the strongest GPT-4 only achieves 38.1% turn-based accuracy and 6.0% session-based accuracy in this task. Other LLMs’ performance is even poorer. The content selection algorithm can partially solve this challenge by filtering out irrelevant file content, but GPT-4 with it still only achieves 43.1% turn-based accuracy. That means current LLMs (e.g., GPT-4) still struggle to handle complex and lengthy PPT templates. For open-source LLMs, there’s a risk of information loss due to token limitations (typically 2 ∼4K tokens limit), which often require truncating lengthy PPT content. When it comes to session-based performance, the accuracy remains nearly zero. This implies that current LLMs are still far from being ideal PPT agents capable of effectively assisting users in editing PPT templates during a multi-turn dialogue session.
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(3) Multi-modal instructions increase the LLM’s failure rate significantly. To assess LLMs’ task completion performance for instructions in-volving multi-modal operations (Table, Chart, Pic-ture, Position, and text), we calculate the average accuracy of GPT-4 for instructions involving each modality, respectively. This is done by dividing the number of correctly completed instructions within each modality by the total number of instructions involving that modality’s operation. The results are presented in Figure 5 (a). From the figure, we observe that GPT-4 performs exceptionally well in the text modality, achieving an accuracy of 85.6%. Its performance becomes poorer when process-ing structured data (Chart and Table), with 12.4% and 16.2% lower accuracy. Instructions involv-ing picture-related operation pose an even greater challenge for GPT-4, as it achieves a 56.8% turn-based accuracy in this modality. GPT-4 exhibits its weakest performance in instructions involving position-related (spatial) operations, with only 24% accuracy. This underscores GPT-4’s limitations in spatial perception ability.
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5 Analysis
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In this section, we analyze the reasons for GPT-4’s errors. We further analyze the influence of model size and dialogue history.
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5.1 Error Analysis of GPT-4 in our benchmark
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To analyze the error made by GPT-4, in our bench-mark, we gather 50 wrong samples for each of the two tasks in our benchmark in the turn-based eval-uation. We find that these wrong samples fall into four error types and visualize the distribution of these four main error types in Figure 5 (b): (1) Po-Figure 5: We illustrate the analysis results of the creating new PPT file task (task 1) and the editing PPT template task (task 2). In sub-figure (a), we report the average turn-based accuracy for instructions involving chart, table, picture, position, and pure text. We don’t draw the accuracy of task 2 as no chart instruction in this task. In sub-figure (b), we report the ratio of four common errors made by GPT-4. In sub-figure (c), we report the accuracy with the model size. We don’t plot the session-based accuracy of task 2 as it is zero.
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sition errors: These occur when GPT-4 struggles with instructions involving position adjustments. For example, when asked to move the shape to the bottom of the slide, GPT-4 wrongly calls the "set _ top" API. position error is the main error in the creating new PPT file task as this task contains many instructions involving position operation. (2) Calling unavailable APIs: GPT-4 sometimes gener-ates APIs that don’t actually exist in the reference API file, resulting in what we call the "API hallu-cination problem." (3) Misunderstanding PPT file content: GPT-4’s comprehension of the PPT con-tent can be flawed, leading to the generation of incorrect API sequences. For example, when in-structed to make the font size of the current slide’s title consistent with previous slides, GPT-4 set a font size that is different from what was used in previous slides’ titles. In the editing template task, misunderstanding the PPT content becomes the main error since this task needs to understand the complex PPT template. (4) Unfollowing Power-point task rules: Completing Powerpoint tasks de-mands a deep understanding of various task rules. For instance, writing a new slide title requires first deleting the original text and then inserting the new title into the text box. However, GPT-4 may di-rectly insert the new content. For the session-based evaluation, we also collect 50 wrong examples. We find that the main reason for the poor session-based performance is the LLM fails to finish the session once it makes an error in one turn of the session. The reasons for errors made in a single turn are similar to those in the turn-based evaluation. One unique phenomenon in this evaluation is that the LLM would repeat previous errors (e.g., employing infeasible APIs) in subsequent turns.
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5.2 Does bigger LLM work better on PPTC?
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To investigate how the model size impacts the LLM’s performance in our benchmark, we con-duct tests using LLaMa-2-chat LLM with 7, 13, and 70 billion parameters and plot the results in Figure 5 (c). We observe that larger LLM consis-tently achieve higher turn-based accuracy for both the creating new PPT and editing PPT template tasks. For example, in the creating new PPT file task, we find that the turn-based accuracy increases from 13.2 (7B) to 30.1 (70B). However, we do not observe a clear positive correlation between model size and session-based performance. One possible explanation is that although the 70B LLM can cor-rectly finish more intermediate steps, it still falls short of completing the entire session. To improve the session-based performance, a larger LLM may be necessary.
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5.3 Does dialogue history help LLMs to generate the API sequence?
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To investigate the influence of dialogue history in our prompt (see Figure 4), we make an ablation experiment for the dialogue history component of our turn-based evaluation prompt §. In this evalua-tion, the dialogue history contains previous turns along with their feasible API sequences. When we removed the dialogue history from the prompt, we observed a decline in GPT-4’s performance. Specif-ically, GPT-4 drops its performance from 75.1 %
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> §The task instruction, current user instruction, API file, PPT content in the prompt are necessary parts for generating the API sequence. So we don’t conduct ablation studies on them.
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to 73.1 % in the creating new PPT file task and decreases its performance by 6.2 % in the editing template task. This experiment shows the positive effect of the dialogue history, as it helps the LLM to both understand the dialogue background and instruct the LLM to correctly use the APIs, similar to few-shot demonstration examples.
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6 Related Works
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Large Language Models like ChatGPT, GPT-4 (Bubeck et al., 2023; OpenAI, 2023), and Bard have billions of parameters and have been trained on the Internet corpus with trillions of tokens. They can write code (Liu et al., 2023a), prove mathemat-ical theorems (Jiang et al., 2022), pass the profes-sional exam (Zhong et al., 2023; Gilson et al., 2023; Katz et al., 2023), and also perform well on other basic natural language tasks (Kim et al., 2023; Jiao et al., 2023; Zhong et al., 2023; Wang et al., 2023b). That raises the hope of achieving artificial general intelligence (AGI). To further boost LLM’s performance on the spe-cific task, one approach involves prompting en-gineerings, such as the chain of thought prompt-ing (Wei et al., 2022; Shi et al., 2022; Yao et al., 2023), self-consistency (Wang et al., 2022) and the least to most prompting (Zhou et al., 2022). An-other approach aims to use feedback to improve per-formance. The self-refine method (Madaan et al., 2023) refines the output through iterative feedback and refinement Provided by LLM itself. The Re-flexion (Shinn et al., 2023) method generates and stores the reflection based on the sparse reward signal and then uses the reflection to induce bet-ter decisions in subsequent trials. The learning to program method (Guo et al., 2023) learns the task program by inducing the general solutions from the errors (feedback) iteratively and uses the program to guide the inference.
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Task completion benchmarks for measuring large language models . To measure LLM’s task completion performance, Saycan (Brohan et al., 2023) and VirtualHome (Puig et al., 2018) bench-marks ask LLM to generate the correct action se-quence for controlling the robot to finish user in-struction. WebShop (Yao et al., 2022) and Android in the wild (Rawles et al., 2023) ask LLM to navi-gate websites and conduct actions to meet the user requirement. APIBench (Patil et al., 2023) and ToolBench (Xu et al., 2023b; Qin et al., 2023b) in-volve selecting and using APIs to complete the task instruction. Agentbench (Liu et al., 2023b)assesses LLM as autonomous agents in 8 environments and WebArena (Zhou et al., 2023) considers task com-pletion in web-based interactions.
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AI assistant system for complex task com-pletion For more complex tasks that involve us-ing tools and utilizing environmental information, there are many strong AI systems (e.g., TaskMa-trix (Liang et al., 2023)) that help the user finish the complex task. One approach involves connecting massive models and tools with the Large Language Model for task completion. Examples include Visual ChatGPT (Wu et al., 2023) and Hugging-GPT (Shen et al., 2023) which use LLM to deploy task-specific models to finish the user instruction based on the observation of task information (e.g., visual information), Voyager (Wang et al., 2023a) that uses the fixed LLM to continually learn skills (tools) based on the observation of the Minecraft environment. Another approach involves training an end-to-end LLM to finish the user instruction. Examples include Gorilla (Patil et al., 2023) for generating API calls to finish the user query by using the API bench to fine-tune the pre-trained LLaMA (Touvron et al., 2023) model. PaLM-E (Driess et al., 2023) for various robot reasoning tasks by fine-tuning the pretrained PaLM (Chowd-hery et al., 2022) model using features from sensor modalities. Different from the above systems, we focus on the research topic of the AI assistant sys-tem in office software.
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7 Conclusion
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We introduce the PowerPoint Task Completion benchmark to measure LLMs’ ability to complete user instructions within the context of the Power-Point software. It contains hundreds of multi-turn sessions with different topics and thousands of in-structions with varying levels of difficulty. We fur-ther propose the PPTX-evaluation system to access and compare the performance of different LLMs. Results show that GPT-4 is the strongest LLM but still performs poorly in finishing entire sessions. We further analyze the behavior of LLMs and find three main error factors that limit their performance. Our benchmark and findings can help the research community design better AI task completion assis-tants. 8 Limitations
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Our benchmark does not consider instructions that involve subjective evaluation. For example, the user may want to make the slide more beautiful. However, it’s hard to automatically evaluate if the generated file (the model output) is more beautiful. Another limitation is that we do not consider the instructions that need non-API operations. For ex-ample, the user may want to draw a cat on the slide. That instruction needs the AI-assistant system to draw the cat by dragging the mouse and is still in-feasible for LLMs and LLM-based systems. We only consider instructions that can be completed by directly executing the API sequence.
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References
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A The API Reference File
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We list all APIs and their descriptions in Figures 6 and 7. We provide 49 feasible APIs.
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B The Prompt for Content Selection Algorithm
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We put the prompt of content selection algorithm in Figure 8. API reference file
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Slide-related APIs
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API: create slide(): This API creates a new slide. API: move to previous slide(): This API moves to the previous slide. API: move to next slide(): This API moves to the next slide. API: move to slide(slide id): This API moves to the slide with given slide id.It takes one parameter ’slide id’, the ID of the slide to move to as a integer.
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Choose-related APIs
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API: choose title(): This API selects the title on the slide. You should first call choose title() before inserting text to or changing font attributes of the title. API: choose content(): This API select the content on the slide. You should first call choose content() before inserting text to or changing font attributes of the content. API: choose textbox(idx): This API selects the textbox element on the slide. It takes one parameter, the index of textbox as integer. idx is set to 0 by default, meaning the first textbox. You should first call choose textbox() before inserting text to or changing font attributes of the textbox element. API: choose picture(idx): This API selects the picture element on the slide. It takes one parameter, the index of textbox as integer. idx is set to 0 by default, meaning the first textbox. You should first call choose picture() before changing height, width, rotation of the picture element. You should not call choose picture() before inserting picture element. API: choose chart(): This API selects the chart element on the slide. You should first call choose chart() before changing the chart. You should not call choose chart() before inserting chart element. API: choose shape(shape name): This API selects a specific shape by shape name on the slide. It takes one parameter ’shape name’, the name of the shape to select as a string. shape name can be chosen from [’rectangle’,’right arrow’,’rounded rectangle’,’triangle’,’callout’,’cloud’,’star’,’circle’] You should first call choose shape(shape name) before you can do operations on the shape. You should not call choose shape(shape name) before inserting shape element. API: choose table(): This API selects the table element on the slide. You should first call choose table() before changing the table. You should not call choose table() before inserting table element. API: choose table cell(row id, column id): This API selects a specific cell in the table by giving row id and column id. It takes two parameters, the row id and column id of the cell to select as integers (id starts from 0). Remember the first parameter is row id, the second parameter is column id. You should first call choose table cell(row id, column id) before inserting text.
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Basic APIs
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API: set background color(color): This API sets the background color of the slide. It takes one parameter ’color’, the color name to set as a string, such as ’red’, ’purple’. API: set width(width): This API sets the width of the selected object. It takes one parameter ’width’, the width of an object in centimeters as float. You should first choose an object before you can change the width of it. API: set height(height): This API sets the height of the selected object. It takes one parameter ’height’, the height of an object in centimeters as float. You should first choose an object before you can change the height of it API: rotate element(angle): This API rotates the selected element by the specified angle. It takes one parameter ’angle’, the angle to rotate clockwise as integer. You should first choose an object before you can rotate it. API: set fill color(color): This API sets the fill color of the selected object after the object is chosen. It takes one parameter ’color’, the color name to set as a string, such as ’red’, ’purple’. You can set the fill color of content, title or textbox. API: set left(left): This API moves and changes the object’s position. It sets the x position of the selected object’s leftmost point. It takes one parameter, the x position to set. You should first choose an object before you can change the left of it API: set top(top): This API moves and changes the object’s position. It sets the y position of the selected object’s upmost point. It takes one parameter, the y position to set. You should first choose an object before you can change the top of it.
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> Figure 6: The reference API file: part 1.
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API reference file
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Text-related APIs
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API: insert text(text): This API inserts text into a text frame (textbox, title, content, table). API: insert bullet point(text): This API inserts a bullet point into the content. It takes one parameter, the text of the bullet point to insert as a string. API: insert note(text): This API inserts a note onto the slide. It takes one parameter, the note text to insert as a string. API: insert textbox(): This API inserts a textbox onto the slide. When you need to add a caption or text under/above/left to/right to an object, you can call insert textbox(). API: delete text(): This API delete the text part of an object. You should first choose content or title before you can call delete text() API: set font size(font size): This API sets the size of the font It can take one argument ’font size’, the font size to set as an integer. API: set font color(color): This API sets the color of the font. It takes one parameter ’color’, the color name to set as a string, such as ’red’, ’purple’. API: set font bold(): This API sets the font to be bold. API: set font italic(): This API sets the font to be italic. API: set font underline(): This API sets the font to be underlined. API: set font style(font name): This API sets the font style of the selected text. It can take one argument ’font style’, the font name as a string. API: set line space(line space level): This API sets the line spacing of the selected text. It can take one argument ’line space level’, as an integer, default 0. API: text align left(): This API aligns the text to left. API: text align center(): This API aligns the text to center. API: text align right(): This API aligns the text to right.
|
| 234 |
+
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| 235 |
+
Image and shape-related APIs
|
| 236 |
+
|
| 237 |
+
API: insert picture(picture name): This API inserts a picture onto the slide. It takes one parameter ’picture name’, the name or description of picture as a string API: insert rectangle(): This API inserts a rectangle or square shape onto the slide. API: insert right arrow(): This API inserts an arrow shape onto the slide. API: insert rounded rectangle(): This API inserts a rounded rectangle shape onto the slide. API: insert triangle(): This API inserts a triangle shape onto the slide. API: insert callout(): This API inserts a callout shape onto the slide. API: insert cloud(): This API inserts a cloud shape onto the slide. API: insert star(): This API inserts a star shape onto the current slide. API: insert circle(): This API inserts a circle or oval shape into the current slide.
|
| 238 |
+
|
| 239 |
+
Table-related APIs
|
| 240 |
+
|
| 241 |
+
API: insert table(row num, col num): This API inserts a table of row num rows and col num columns onto the current slide. It takes two argument, the row number and the column number of the inserted table as integer. Remember the first parameter is row number and the second parameter is column number. API: insert table row(row data): This API inserts a row (list) of data into the table. It takes one argument, the data to insert as a list of numbers or strings. You should first call choose table() before you can call insert table row(). The parameter ’row data’ should be a list of strings.
|
| 242 |
+
|
| 243 |
+
Chart-related APIs
|
| 244 |
+
|
| 245 |
+
API: insert line chart(data, series): This API inserts a line chart onto the slide. It takes two argument, ’data’ is a list of numbers and ’series’ is a list of strings. API: insert bar chart(data, series): This API inserts a bar chart onto the slide. It takes two argument, ’data’ is a list of numbers and ’series’ is a list of strings. API: insert pie chart(data, series): This API inserts a pie chart onto the slide. It takes two argument, ’data’ is a list of numbers and ’series’ is a list of strings. API: set chart title(title): This API sets the title of a previously inserted chart. It takes one argument ’title’, the title to be set as a string.
|
| 246 |
+
|
| 247 |
+
> Figure 7: The reference API file: part 2.
|
| 248 |
+
|
| 249 |
+
Content Selection prompt
|
| 250 |
+
|
| 251 |
+
You are an AI assistant for PowerPoint. Your task is to determine what kind of content is necessary to fulfill the user’s instruction. You have an API to extract the content, please call the get _content api with correct parameters to fulfill the user’s instruction. You need to extract the minimum necessary information to fulfill user’s instruction.
|
| 252 |
+
|
| 253 |
+
Get _content API: get _ content(need _text: Indicates whether text information is required. The text infor-mation encompasses text in title, content, textbox, table, chart, and shape. This parameter is particularly useful when inserting or modifying text of title, content, textbox, table, chart, and shape, or when information about these objects is essential. need _style: Indicates whether style information is required. Style information includes attributes like font type, font size, color, background color, line space, bold, undeline, italic and other visual aspects of objects like rotation. This is useful when changing the appearance of text or objects or when information about an object’s appearance is essential. need _ position: Indicates whether position information is required. The position details encompass an object’s height, width, and its left and top positions. This is crucial when moving objects or altering an object’s size. need_title: Determines if information related to the title is required. need_content: Determines if information related to the content is required. need_picture: Determines if information related to the picture is required. need_table: Determines if information related to the table is required. need_chart: Determines if information related to the chart is required. need_textbox: Determines if information related to the textbox is required. need _shape: Determines if information related to the shapes (rectangle, right arrow, rounded rectangle, triangle, callout, cloud, star, circle) is required. ) Where the parameters are either 1 (needed) or 0 (not needed). You should only answer with calling get _content() with the right parameters.
|
| 254 |
+
|
| 255 |
+
For examples:
|
| 256 |
+
|
| 257 |
+
Instruction: Increase the font size of the content to 20. Explanation: For information, style information (font size) is needed. For objects, content is needed. Answer: get_content(need_text=1,need_style=1,need_position=0, need_title=0,need_content=1,need_picture=0,need_ table=0,need_chart=0,need_textbox=0,need_shape=0)
|
| 258 |
+
|
| 259 |
+
... Given the instruction, output the Answer without Explanation:
|
| 260 |
+
|
| 261 |
+
Instruction: <Current user instruction> Answer:
|
| 262 |
+
|
| 263 |
+
Figure 8: The prompt of the content selection algorithm.
|
articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/00_task-source-url/images.json
ADDED
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{
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"extracted_count": 0,
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"downloaded_count": 0,
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"failed_count": 0,
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"downloaded": [],
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"failed": [],
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"pruned": [],
|
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+
"pruned_count": 0
|
| 9 |
+
}
|
articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/00_task-source-url/meta.json
ADDED
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{
|
| 2 |
+
"task_id": "04578141-1d42-4146-b9cf-6fab4ce5fd74",
|
| 3 |
+
"category": "libreoffice_impress",
|
| 4 |
+
"instruction": "Change the text color in the textboxes to on slide 1 yellow, red, and green, respectively, in top-to-bottom order. Use exactly these colors—no variations (e.g., no dark red, light green, etc.).",
|
| 5 |
+
"task_source": "https://arxiv.org/pdf/2311.01767.pdf",
|
| 6 |
+
"search_query": "LibreOffice Impress Change the text color in the textboxes to on slide 1 yellow, red, and green, respectively, in top-to-bottom order. Use exactly these colors—no variations (e.g., no dark red, light green, etc.). tutorial guide how to",
|
| 7 |
+
"rank": 0,
|
| 8 |
+
"title": "Task source URL",
|
| 9 |
+
"url": "https://arxiv.org/pdf/2311.01767.pdf",
|
| 10 |
+
"domain": "arxiv.org",
|
| 11 |
+
"snippet": "Ground-truth source URL from the OSWorld task JSON.",
|
| 12 |
+
"serper_source": "task_source",
|
| 13 |
+
"fetch": {
|
| 14 |
+
"ok": true,
|
| 15 |
+
"method": "jina",
|
| 16 |
+
"status_code": 200,
|
| 17 |
+
"final_url": "https://r.jina.ai/https://arxiv.org/pdf/2311.01767.pdf",
|
| 18 |
+
"error": null,
|
| 19 |
+
"fetched_at_unix": 1778968882
|
| 20 |
+
},
|
| 21 |
+
"paths": {
|
| 22 |
+
"article_markdown": "/Users/mercury/Desktop/Research/NIPS26-MetaAgent-RL/OSWorld-private/tutorial_serper_rag_firecrawl/articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/00_task-source-url/article_markdown.md",
|
| 23 |
+
"meta_json": "/Users/mercury/Desktop/Research/NIPS26-MetaAgent-RL/OSWorld-private/tutorial_serper_rag_firecrawl/articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/00_task-source-url/meta.json",
|
| 24 |
+
"serper_result_json": "/Users/mercury/Desktop/Research/NIPS26-MetaAgent-RL/OSWorld-private/tutorial_serper_rag_firecrawl/articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/00_task-source-url/serper_result.json",
|
| 25 |
+
"firecrawl_raw_json": null,
|
| 26 |
+
"images_json": "/Users/mercury/Desktop/Research/NIPS26-MetaAgent-RL/OSWorld-private/tutorial_serper_rag_firecrawl/articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/00_task-source-url/images.json"
|
| 27 |
+
},
|
| 28 |
+
"content": {
|
| 29 |
+
"word_count": 11342,
|
| 30 |
+
"sha256": "8336e5020516863f688354523927eb669a5356c4cd591df9274f0d9aa2da3db9"
|
| 31 |
+
},
|
| 32 |
+
"images": {
|
| 33 |
+
"extracted_count": 0,
|
| 34 |
+
"downloaded_count": 0,
|
| 35 |
+
"failed_count": 0
|
| 36 |
+
}
|
| 37 |
+
}
|
articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/00_task-source-url/serper_result.json
ADDED
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@@ -0,0 +1,6 @@
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+
{
|
| 2 |
+
"title": "Task source URL",
|
| 3 |
+
"link": "https://arxiv.org/pdf/2311.01767.pdf",
|
| 4 |
+
"snippet": "Ground-truth source URL from the OSWorld task JSON.",
|
| 5 |
+
"source": "task_source"
|
| 6 |
+
}
|
articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/01_how-do-you-change-text-color-in-libreoffice-impress/article_markdown.md
ADDED
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@@ -0,0 +1,363 @@
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|
| 1 |
+
Title: How do you change text color in LibreOffice Impress?
|
| 2 |
+
|
| 3 |
+
URL Source:
|
| 4 |
+
|
| 5 |
+
Markdown Content:
|
| 6 |
+
# presentation - How do you change text color in LibreOffice Impress? - Ask Ubuntu
|
| 7 |
+
|
| 8 |
+
* Ubuntu
|
| 9 |
+
* Community
|
| 10 |
+
* Ask!
|
| 11 |
+
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|
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|
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|
| 21 |
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* Launchpad
|
| 22 |
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* MAAS
|
| 23 |
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* Canonical
|
| 24 |
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|
| 25 |
+
[](
|
| 26 |
+
|
| 27 |
+
#### Stack Exchange Network
|
| 28 |
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|
| 29 |
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Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.
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Visit Stack Exchange
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1. []( "Help Center and other resources")
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* Tour Start here for a quick overview of the site
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2. []( "A list of all 183 Stack Exchange sites")
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# How do you change text color in LibreOffice Impress?
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Ask Question
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Asked 8 years, 5 months ago
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Modified5 years, 1 month ago
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Viewed 26k times
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[](
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How do I change slide text color?
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I don't see a format text menu ribbon like I am accustomed to seeing in Microsoft Powerpoint, nor do I see a collection of text editing options like in Google Documents.
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* libreoffice
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* presentation
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asked Dec 4, 2017 at 1:04
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Image 6: DBinJP's user avatar
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DBinJP
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* 3 I do not see where the text color is displayed in the toolbar, so I am unable to follow the instruction at help.libreoffice.org/Common/Changing_the_Color_of_Text I have already tried Line Color and Fill Color, and neither of these change the text color.DBinJP –DBinJP 2017-12-04 01:12:25 +00:00 Commented Dec 4, 2017 at 1:12
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Add a comment|[]( "Expand to show all comments on this post")
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## 3 Answers 3
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Sorted by: Reset to default
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You can also add the "Text Formatting" toolbar to get access to all text formatting options. View > Toolbars > Text Formatting.
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Sorry, LibreOffice, but this is quite non-intuitive (i.e. dumb). When one highlights text, the text formatting toolbar should automatically appear. No?
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answered Nov 29, 2019 at 0:13
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RudyF
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* This is a best answer. But the panel will hide as soon as you select a non-text element. So, you will have to add it as a third panel, and it will show up when you edit text. Another way is to select a text, right click on it and choose "Characters" (or "symbols"). Then you can change the color on the "Fonts Effect" tab: ask.libreoffice.org/t/draw-how-to-change-text-color/54772gearcoded –gearcoded 2022-12-18 08:10:04 +00:00 Commented Dec 18, 2022 at 8:10
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Found via searching menu bar Help > LibreOffice Help for 'color': Click View > Toolbars > Color Bar (scroll down; it is not alphabetized but rather at the bottom of the list), then with the text highlighted, click on the desired color panel in the bottom Color Bar (panel that appears beneath the slide).
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answered Dec 4, 2017 at 1:15
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Image 8: DBinJP's user avatar
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DBinJP
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* 5 this is not the best design decision by whoever designed libreoffice draw .jeremy_rutman –jeremy_rutman 2019-11-13 14:18:06 +00:00 Commented Nov 13, 2019 at 14:18
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* I think the developers need to sit down with the users and have a discussion.deltaray –deltaray 2021-06-18 20:24:55 +00:00 Commented Jun 18, 2021 at 20:24
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* 1 "Color bar" toolbar does not exist anymore (or at least in my version). On the upside, the list does seem sorted. I had to use the "text formatting" toolbar as mentioned in @RudyF 's answer mx1up –mx1up 2021-07-10 10:02:42 +00:00 Commented Jul 10, 2021 at 10:02
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* Cowboy developers...kensaii –kensaii 2021-11-09 22:20:30 +00:00 Commented Nov 9, 2021 at 22:20
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Edit: I ended up here by searching "libreoffice impress font color formula", so there is my solution for coloring the Formula-Text
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You can use coloring in your ormula by using the `color` tag:
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```
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color <color> {your math expression}
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```
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I wasn't able to use hex-Color-Codes, but only words, like
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white black red green blue
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There might be more usable colors.
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I found it in the OpenOffice-Forum, but it works in LibreOffice too:
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answered Apr 10, 2021 at 20:23
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6")How to apply master font and color to existing slides in LibreOffice Impress
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Image 12: .
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/01_how-do-you-change-text-color-in-libreoffice-impress/images.json
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{
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"extracted_count": 6,
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"downloaded_count": 3,
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"downloaded": [
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{
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"url": "https://askubuntu.com/Content/Sites/askubuntu/Img/logo.svg?v=472cf2768bba",
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"path": "/Users/mercury/Desktop/Research/NIPS26-MetaAgent-RL/OSWorld-private/tutorial_serper_rag_firecrawl/articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/01_how-do-you-change-text-color-in-libreoffice-impress/images/logo.svg",
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|
| 1 |
+
Title: How To Do. Changing The Text Font Color In Libre Impress. #Libre Impress. #Text Font #Color
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| 2 |
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| 3 |
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URL Source:
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| 4 |
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|
| 5 |
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Markdown Content:
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# How To Do. Changing The Text Font Color In Libre Impress. #Libre Impress. #Text Font #Color - YouTube
|
| 7 |
+
|
| 8 |
+
Back Image 2: Meet the music reviewer community
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[](
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Image 3: Meet the music reviewer community
|
| 19 |
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|
| 20 |
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[](
|
| 21 |
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|
| 22 |
+
Video 2
|
| 23 |
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|
| 24 |
+
How To Do. Changing The Text Font Color In Libre Impress. #Libre Impress. #Text Font #Color
|
| 25 |
+
|
| 26 |
+
Tap to unmute
|
| 27 |
+
|
| 28 |
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Image 4
|
| 29 |
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|
| 30 |
+
## How To Do. Changing The Text Font Color In Libre Impress. #Libre Impress. #Text Font #Color
|
| 31 |
+
|
| 32 |
+
Dedekind Music and Community Support 2,289 views 2 years ago
|
| 33 |
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|
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Copy link
|
| 35 |
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|
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Image 5
|
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Image 6
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# How To Do. Changing The Text Font Color In Libre Impress. #Libre Impress. #Text Font #Color
|
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|
| 76 |
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Image 8
|
| 77 |
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Dedekind Music and Community Support
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Dedekind Music and Community Support
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576 subscribers
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Subscribed
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2.2K views 2 years agoLearning To Share
|
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| 90 |
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2,289 views • Jul 30, 2023 • Learning To Share
|
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|
| 92 |
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1. Select the text that you want changed. 2. Select the Format drop down menu. 3. Choose and select the Character menu.…...more
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Image 11 ### Dedekind Music and Community Support 576 subscribers
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[]( To Share
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# How To Do. Changing The Text Font Color In Libre Impress. #Libre Impress. #Text Font #Color
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2,289 views 2.2K views
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Jul 30, 2023
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Save
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Download
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Download
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## Comments 6
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Top Show featured commentsNewest Show recent comments, including potential spam
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## In this video
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Transcript
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## Description
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How To Do. Changing The Text Font Color In Libre Impress. #Libre Impress. #Text Font #Color
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14 Likes
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2,289 Views
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2023 Jul 30
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1. Select the text that you want changed. 2. Select the Format drop down menu. 3. Choose and select the Character menu. 4. Select the Font Effects Tab. 5. Select the required Font Color. confirm by pressing the OK button.…...more
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...more Show less
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Explore the podcast
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Image 145 episodes 5 episodes
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Learning To Share
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Image 15 Podcasts
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Follow along using the transcript.
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Show transcript
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Image 16 ### Dedekind Music and Community Support 576 subscribers
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VideosAboutImage 17 SoundcloudImage 18 X
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## Transcript
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NaN / NaN
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Image 19 9:22
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Image 20
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### Optimizing LibreOffice To Work Like Microsoft Office ...
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Michael Horn
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Image 21 9:17
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Image 22
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### Impress(29) - Interaction
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MichaelsTechTutorials
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Image 23 LIVE
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Image 24
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### Livestream, "Good Luck Charm" Lucky Crane Couple, Screensaver.TV Picture Art.
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Get Inspired By Art
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Image 25 12:16
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Image 26
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### Microsoft Office vs LibreOffice | How to Make the Change
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Chris Titus Tech
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423K views • 6 years ago
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Image 27 LIVE
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Image 28
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### Unexpected Massive Explosion In Israel - w/ Ex- CIA Larry Johnson
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Image 29 4:18
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Image 30
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### Hooters Waitress - SNL
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Saturday Night Live
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8.6M views • 2 years ago
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Image 31 5:45
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Image 32
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### ADHD Child vs. Non-ADHD Child Interview
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Mental Health Matters
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28M views • 10 years ago
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Image 33 8:32
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Image 34
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### LAWYER: If Cops Say "I Smell Alcohol" - Say THESE WORDS
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Hampton Law
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2.6M views • 2 months ago
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Image 35 4:46
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Image 36
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### Airbnb Superhost - SNL
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Saturday Night Live
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1.8M views • 1 month ago
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Image 37 3:00:11
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### 3 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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683K views • 2 years ago
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Image 39 30:01
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### Soft Pastel Sunrise Gradient Background for 30 Minutes
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### Pink Ombre Aura Screen | 3 Hours and 1 Second | No Sound
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Set the Mood
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Image 43 2:30:00
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Image 44
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### 2 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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65K views • 1 year ago
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Image 45 30:13
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Image 46
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### Abstract Multicolored Geometric lines Background video | Footage | Screensaver
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MG1010
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620K views • 6 years ago
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Image 47 2:00:18
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Image 48
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### Baby Blue Aura | Background | To relax | HD 4K
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aesthetic spirit
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28K views • 4 months ago
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Image 49 3:00:53
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Image 50
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### Light aura background light
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𝐢𝐫𝐞ꨄ
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Image 51 2:00:01
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Image 52
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### FIELD OF BLOOMS | FRAMED TV ART WALLPAPER SCREENSAVER BACKGROUND | 2 HRS
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Image 53 3:00:00
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Image 54
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### Wildflower Meadow | Turn Your TV Into Art | Vintage Oil Painting Screensaver | TV Framed Art | 3 Hrs
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Oil & Ambience
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Image 56
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### Vintage While Panting | Screensaver for TV Wallpaper #tvwallpaper #tv #vintagepainting #frame #glod
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Amazing Avira
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Image 58
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### A Floral Dream in Pastel Tones| Dreamy Spring Floral Art for Frame TV | 4K Screensaver
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Show more
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/02_how-to-do-changing-the-text-font-color-in-libre-impress/images.json
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| 1 |
+
Title: How to Change Font Color in LibreOffice Impress
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URL Source:
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Markdown Content:
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# How to Change Font Color in LibreOffice Impress - YouTube
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Back Image 1: Meet the music reviewer community
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Skip navigation
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Search
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Search with your voice
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[](
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Image 2: Meet the music reviewer community
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[](
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Video 2
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How to Change Font Color in LibreOffice Impress
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Tap to unmute
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Image 3
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## How to Change Font Color in LibreOffice Impress
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Master of Tutorials 438 views 6 months ago
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Copy link
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Info
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Shopping
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Image 4
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Image 5
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If playback doesn't begin shortly, try restarting your device.
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Videos you watch may be added to the TV's watch history and influence TV recommendations. To avoid this, cancel and sign in to YouTube on your computer.
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Cancel Confirm
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Share
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[]( "Share link")- [x] Include playlist
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Image 6
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0:00
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[]( "Next (SHIFT+n)")
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0:00 / 0:00
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Live
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•Watch full video
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Storchennest Live Webcam in Bad Salzungen, Thüringen Stadtverwaltung Bad Salzungen 2K watching • 3 months ago Live Playlist ()Mix (50+)Livestream, "Good Luck Charm" Lucky Crane Couple, Screensaver.TV Picture Art.Get Inspired By Art 1.9K watching • 1 day ago Live Playlist ()Mix (50+)2:00:18 Baby Blue Aura | Background | To relax | HD 4K aesthetic spirit 28K views • 4 months ago Live Playlist ()Mix (50+)3:00:01 Pastel Pink Screen | 3 Hours and 1 Second | No Sound Set the Mood 433K views • 2 years ago Live Playlist ()Mix (50+)1:00:04 pantalla a100 aro luz blanco Milton Cubillos 1.2M views • 5 years ago Live Playlist ()Mix (50+)2:30:00 2 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV Pigment Peace 65K views • 1 year ago Live Playlist ()Mix (50+)3:00:01 Pink Ombre Aura Screen | 3 Hours and 1 Second | No Sound Set the Mood 2.6M views • 2 years ago Live Playlist ()Mix (50+)3:00:00 Wildflower Meadow | Turn Your TV Into Art | Vintage Oil Painting Screensaver | TV Framed Art | 3 Hrs Oil & Ambience 7.9K views • 2 months ago Live Playlist ()Mix (50+)2:00:01 Vibrant Yellow Flower Oil Painting for TV | Frame Art TV Screensaver | 4K Visuals | 2 Hours Frame TV Art Gallery Wallpapers 3.9K views • 1 year ago Live Playlist ()Mix (50+)3:00:11 3 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV Pigment Peace 683K views • 2 years ago Live Playlist ()Mix (50+)[3:00:01 [3 Hours Loop]4K Gradient Mood Lights Purple Orange |Smooth LED Lights for Background Yill Bake 2.6M views • 2 years ago Live Playlist ()Mix (50+)]( Pink Hue Wallpaper Screen Seraphic Glow 472K views • 2 years ago Live Playlist ()Mix (50+)
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Sign in to confirm you’re not a bot This helps protect our community. Learn more
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# How to Change Font Color in LibreOffice Impress
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Image 7
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Master of Tutorials
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Master of Tutorials
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1.18K subscribers
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Join
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Subscribed
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Download
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Download
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438 views 6 months agoSTANY ZJEDNOCZONE
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438 views • Oct 23, 2025 • STANY ZJEDNOCZONE
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| 99 |
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| 100 |
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Learn how to change the font color in LibreOffice Impress slides to make your text stand out and improve presentation design. In LibreOffice Impress, you can easily change the font color of your slide text to match your presentation theme or emphasize key points. You can apply solid colors, gradients, or custom RGB values for complete design control.…...more
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...more
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How this was made
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Auto-dubbed
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Audio tracks for some languages were automatically generated. Learn more
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Transcript
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Follow along using the transcript.
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Show transcript
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Image 8 ### Master of Tutorials 1.18K subscribers
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VideosAbout
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Image 931 31
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Libreoffice Impress Course
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by Master of Tutorials
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| 126 |
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Show less
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[]( ZJEDNOCZONE
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# How to Change Font Color in LibreOffice Impress
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+
438 views 438 views
|
| 132 |
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| 133 |
+
Oct 23, 2025
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| 135 |
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Save
|
| 136 |
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| 137 |
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## 0 Comments
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Sort comments
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Sort by
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Top Show featured commentsNewest Show recent comments, including potential spam
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Add a comment...
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## Comments
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| 151 |
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Top Show featured commentsNewest Show recent comments, including potential spam
|
| 152 |
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|
| 153 |
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## In this video
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| 154 |
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|
| 155 |
+
Transcript
|
| 156 |
+
|
| 157 |
+
## Description
|
| 158 |
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How to Change Font Color in LibreOffice Impress
|
| 160 |
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3 Likes
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| 162 |
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| 163 |
+
438 Views
|
| 164 |
+
|
| 165 |
+
Oct 23 2025
|
| 166 |
+
|
| 167 |
+
Learn how to change the font color in LibreOffice Impress slides to make your text stand out and improve presentation design. In LibreOffice Impress, you can easily change the font color of your slide text to match your presentation theme or emphasize key points. You can apply solid colors, gradients, or custom RGB values for complete design control. #libreoffice#libreofficetutorial#libreofficeimpress I’d really appreciate it if you subscribed to my channel: Image 11/@masteroftutorials-1 , 👉 Check and buy my graphics here: 👉 With my link you can find promotions up to 80% on useful gadgets
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| 168 |
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| 169 |
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...more Show less
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| 170 |
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| 171 |
+
How this was made
|
| 172 |
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| 173 |
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Auto-dubbed
|
| 174 |
+
|
| 175 |
+
Audio tracks for some languages were automatically generated. Learn more
|
| 176 |
+
|
| 177 |
+
Follow along using the transcript.
|
| 178 |
+
|
| 179 |
+
Show transcript
|
| 180 |
+
|
| 181 |
+
Image 12 ### Master of Tutorials 1.18K subscribers
|
| 182 |
+
|
| 183 |
+
VideosAbout
|
| 184 |
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|
| 185 |
+
Image 1331 31
|
| 186 |
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|
| 187 |
+
Libreoffice Impress Course
|
| 188 |
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|
| 189 |
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by Master of Tutorials
|
| 190 |
+
|
| 191 |
+
## Transcript
|
| 192 |
+
|
| 193 |
+
NaN / NaN
|
| 194 |
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Image 14 LIVE
|
| 196 |
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Image 15
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| 199 |
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### Storchennest Live Webcam in Bad Salzungen, Thüringen
|
| 200 |
+
|
| 201 |
+
Stadtverwaltung Bad Salzungen
|
| 202 |
+
|
| 203 |
+
2K watching
|
| 204 |
+
|
| 205 |
+
Image 16 LIVE
|
| 206 |
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|
| 207 |
+
Image 17
|
| 208 |
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|
| 209 |
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### Livestream, "Good Luck Charm" Lucky Crane Couple, Screensaver.TV Picture Art.
|
| 210 |
+
|
| 211 |
+
Get Inspired By Art
|
| 212 |
+
|
| 213 |
+
1.9K watching
|
| 214 |
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|
| 215 |
+
Image 18 2:00:18
|
| 216 |
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Image 19
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|
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### Baby Blue Aura | Background | To relax | HD 4K
|
| 220 |
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|
| 221 |
+
aesthetic spirit
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| 222 |
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| 223 |
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28K views • 4 months ago
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Image 20 3:00:01
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Image 21
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### Pastel Pink Screen | 3 Hours and 1 Second | No Sound
|
| 230 |
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|
| 231 |
+
Set the Mood
|
| 232 |
+
|
| 233 |
+
433K views • 2 years ago
|
| 234 |
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Image 22 1:00:04
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Image 23
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### pantalla a100 aro luz blanco
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Milton Cubillos
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1.2M views • 5 years ago
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Image 24 2:30:00
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Image 25
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### 2 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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| 250 |
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Pigment Peace
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| 252 |
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65K views • 1 year ago
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Image 26 3:00:01
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Image 27
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### Pink Ombre Aura Screen | 3 Hours and 1 Second | No Sound
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Set the Mood
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2.6M views • 2 years ago
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Image 28 3:00:00
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Image 29
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### Wildflower Meadow | Turn Your TV Into Art | Vintage Oil Painting Screensaver | TV Framed Art | 3 Hrs
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Oil & Ambience
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7.9K views • 2 months ago
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Image 30 2:00:01
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Image 31
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### Vibrant Yellow Flower Oil Painting for TV | Frame Art TV Screensaver | 4K Visuals | 2 Hours
|
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Frame TV Art Gallery Wallpapers
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3.9K views • 1 year ago
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Image 32 3:00:11
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Image 33
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### 3 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
|
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Pigment Peace
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683K views • 2 years ago
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Image 34 3:00:01
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Image 35
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### [[3 Hours Loop]4K Gradient Mood Lights Purple Orange |Smooth LED Lights for Background](
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Yill Bake
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2.6M views • 2 years ago
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Image 36 3:00:01
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Image 37
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### Pink Hue Wallpaper Screen
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Seraphic Glow
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472K views • 2 years ago
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Image 38 3:00:53
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Image 39
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### Light aura background light
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𝐢𝐫𝐞ꨄ
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143K views • 2 months ago
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Image 40 30:41
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Image 41
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### Gold Vip 3D Text Background video | Footage | Screensaver
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MG1010
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86K views • 6 years ago
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Image 42 1:00:04
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Image 43
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### Softbox Lighting | Softbox Light | Photography Lighting
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Useful Tools
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237K views • 5 years ago
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Image 44 3:00:01
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Image 45
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### Vintage While Panting | Screensaver for TV Wallpaper #tvwallpaper #tv #vintagepainting #frame #glod
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Amazing Avira
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2.6K views • 10 months ago
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Image 46 2:00:08
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Image 47
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### Spring Frame TV Art 🌸 Wildflower Meadow at Sunset | Gold Frame TV Art | Art Screensaver for TV
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Lux Frame Studio
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8.7K views • 3 months ago
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Image 48 2:00:01
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Image 49
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### Frame TV Still Life TV Art Screensaver | Morning Coffee
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Art for Your Frame TV
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61K views • 10 months ago
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Image 50 3:00:01
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Image 51
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### Moody Floral Framed Art Screensaver | Elegant Botanical Collection | 6 Different Wall Art Displays
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Hearthside Wall Art
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36K views • 3 months ago
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Image 52 2:00:00
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Image 53
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### Vintage Floral Free Tv Art Wallpaper Screensaver Home Decor Samsung Oil Painting Digital Wildflower
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LunaSkyeTvArt
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2M views • 2 years ago
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Show more
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/03_how-to-change-font-color-in-libreoffice-impress/images.json
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/03_how-to-change-font-color-in-libreoffice-impress/meta.json
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"images": {
|
| 33 |
+
"extracted_count": 15,
|
| 34 |
+
"downloaded_count": 14,
|
| 35 |
+
"failed_count": 1
|
| 36 |
+
}
|
| 37 |
+
}
|
articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/03_how-to-change-font-color-in-libreoffice-impress/serper_result.json
ADDED
|
@@ -0,0 +1,7 @@
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| 1 |
+
{
|
| 2 |
+
"title": "How to Change Font Color in LibreOffice Impress",
|
| 3 |
+
"link": "https://www.youtube.com/watch?v=89bC8-qsWKA",
|
| 4 |
+
"snippet": "In LibreOffice Impress, you can easily change the font color of your slide text to match your presentation theme or emphasize key points ...",
|
| 5 |
+
"date": "6 months ago",
|
| 6 |
+
"position": 3
|
| 7 |
+
}
|
articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/04_libreoffice-how-to-change-text-color/article_markdown.md
ADDED
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|
| 1 |
+
Title: Libreoffice How To Change Text Color
|
| 2 |
+
|
| 3 |
+
URL Source:
|
| 4 |
+
|
| 5 |
+
Markdown Content:
|
| 6 |
+
# Libreoffice How To Change Text Color - YouTube
|
| 7 |
+
|
| 8 |
+
Back Image 1: Meet the music reviewer community
|
| 9 |
+
|
| 10 |
+
Skip navigation
|
| 11 |
+
|
| 12 |
+
Search
|
| 13 |
+
|
| 14 |
+
Search with your voice
|
| 15 |
+
|
| 16 |
+
[](
|
| 17 |
+
|
| 18 |
+
Image 2: Meet the music reviewer community
|
| 19 |
+
|
| 20 |
+
[](
|
| 21 |
+
|
| 22 |
+
Video 2
|
| 23 |
+
|
| 24 |
+
Libreoffice How To Change Text Color
|
| 25 |
+
|
| 26 |
+
Tap to unmute
|
| 27 |
+
|
| 28 |
+
Image 3
|
| 29 |
+
|
| 30 |
+
## Libreoffice How To Change Text Color
|
| 31 |
+
|
| 32 |
+
Mufasu CAD 3,309 views 3 years ago
|
| 33 |
+
|
| 34 |
+
Copy link
|
| 35 |
+
|
| 36 |
+
Info
|
| 37 |
+
|
| 38 |
+
Shopping
|
| 39 |
+
|
| 40 |
+
Image 4
|
| 41 |
+
|
| 42 |
+
Image 5
|
| 43 |
+
|
| 44 |
+
If playback doesn't begin shortly, try restarting your device.
|
| 45 |
+
|
| 46 |
+
You're signed out
|
| 47 |
+
|
| 48 |
+
Videos you watch may be added to the TV's watch history and influence TV recommendations. To avoid this, cancel and sign in to YouTube on your computer.
|
| 49 |
+
|
| 50 |
+
Cancel Confirm
|
| 51 |
+
|
| 52 |
+
Share
|
| 53 |
+
|
| 54 |
+
[]( "Share link")- [x] Include playlist
|
| 55 |
+
|
| 56 |
+
An error occurred while retrieving sharing information. Please try again later.
|
| 57 |
+
|
| 58 |
+
Image 6
|
| 59 |
+
|
| 60 |
+
0:00
|
| 61 |
+
|
| 62 |
+
[]( "Next (SHIFT+n)")
|
| 63 |
+
|
| 64 |
+
0:00 / 0:00
|
| 65 |
+
|
| 66 |
+
Live
|
| 67 |
+
|
| 68 |
+
•Watch full video
|
| 69 |
+
|
| 70 |
+
In this video
|
| 71 |
+
|
| 72 |
+
Livestream, "Good Luck Charm" Lucky Crane Couple, Screensaver.TV Picture Art.Get Inspired By Art 2K watching • 1 day ago Live Playlist ()Mix (50+)8:21 Impress (20) – Fontwork MichaelsTechTutorials 9.6K views • 9 years ago Live Playlist ()Mix (50+)Storchennest Live Webcam in Bad Salzungen, Thüringen Stadtverwaltung Bad Salzungen 2K watching • 3 months ago Live Playlist ()Mix (50+)1:42 How to Change Font Color in LibreOffice MrQuick 1.4K views • 1 year ago Live Playlist ()Mix (50+)3:00:01 3-Hour Serene Yellow & Orange Gradient - Uplift and Relax Your Space ONCE REELED 418K views • 2 years ago Live Playlist ()Mix (50+)2:00:18 Baby Blue Aura | Background | To relax | HD 4K aesthetic spirit 28K views • 4 months ago Live Playlist ()Mix (50+)2:30:00 2 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV Pigment Peace 65K views • 1 year ago Live Playlist ()Mix (50+)1:00:04 pantalla a100 aro luz blanco Milton Cubillos 1.2M views • 5 years ago Live Playlist ()Mix (50+)3:00:01 Pink Hue Wallpaper Screen Seraphic Glow 472K views • 2 years ago Live Playlist ()Mix (50+)Moody Gardens Penguin Cam LIVE | Penguin Habitat Stream at the Aquarium in Galveston, Texas Moody Gardens 985 watching • 1 month ago Live Playlist ()Mix (50+)3:00:53 Light aura background light 𝐢𝐫𝐞ꨄ 143K views • 2 months ago Live Playlist ()Mix (50+)1:00:01 Bright Flying Green Fire Sparks Background video | Footage | Screensaver MG1010 313K views • 5 years ago Live Playlist ()Mix (50+)
|
| 73 |
+
|
| 74 |
+
Sign in to confirm you’re not a bot This helps protect our community. Learn more
|
| 75 |
+
|
| 76 |
+
# Libreoffice How To Change Text Color
|
| 77 |
+
|
| 78 |
+
Image 7
|
| 79 |
+
|
| 80 |
+
Mufasu CAD
|
| 81 |
+
|
| 82 |
+
Mufasu CAD
|
| 83 |
+
|
| 84 |
+
230K subscribers
|
| 85 |
+
|
| 86 |
+
Join
|
| 87 |
+
|
| 88 |
+
Subscribed
|
| 89 |
+
|
| 90 |
+
Share
|
| 91 |
+
|
| 92 |
+
Save
|
| 93 |
+
|
| 94 |
+
Download
|
| 95 |
+
|
| 96 |
+
Download
|
| 97 |
+
|
| 98 |
+
3.3K views 3 years ago#libreoffice#howto#mufasucad
|
| 99 |
+
|
| 100 |
+
3,309 views • Mar 13, 2023 • #libreoffice #howto #mufasucad
|
| 101 |
+
|
| 102 |
+
learn libreoffice basic tutorial for beginner how to change all text color fast and easy #libreoffice#howto#mufasucad…...more
|
| 103 |
+
|
| 104 |
+
...more
|
| 105 |
+
|
| 106 |
+
## Music 1 songs
|
| 107 |
+
|
| 108 |
+
Image 8 # Back In The City #### The 126ers Back In The City
|
| 109 |
+
|
| 110 |
+
Music
|
| 111 |
+
|
| 112 |
+
Transcript
|
| 113 |
+
|
| 114 |
+
Follow along using the transcript.
|
| 115 |
+
|
| 116 |
+
Show transcript
|
| 117 |
+
|
| 118 |
+
Image 9 ### Mufasu CAD 230K subscribers
|
| 119 |
+
|
| 120 |
+
VideosAboutImage 10 FacebookImage 11 LinkedInImage 12 Twitter
|
| 121 |
+
|
| 122 |
+
Image 13: true 3:13 3:13Gimp How To Bend Text by Mufasu CAD
|
| 123 |
+
|
| 124 |
+
Image 14: true 1:39 1:39Solid Edge How To Delete Relationships by Mufasu CAD
|
| 125 |
+
|
| 126 |
+
Image 15: true 1:10 1:10Blender How To Export UV Map by Mufasu CAD
|
| 127 |
+
|
| 128 |
+
Show less
|
| 129 |
+
|
| 130 |
+
# Libreoffice How To Change Text Color
|
| 131 |
+
|
| 132 |
+
3,309 views 3.3K views
|
| 133 |
+
|
| 134 |
+
Mar 13, 2023
|
| 135 |
+
|
| 136 |
+
Save
|
| 137 |
+
|
| 138 |
+
## 2 Comments
|
| 139 |
+
|
| 140 |
+
Sort comments
|
| 141 |
+
|
| 142 |
+
Sort by
|
| 143 |
+
|
| 144 |
+
Top Show featured commentsNewest Show recent comments, including potential spam
|
| 145 |
+
|
| 146 |
+
Image 16: Default profile photo
|
| 147 |
+
|
| 148 |
+
Add a comment...
|
| 149 |
+
|
| 150 |
+
Image 17
|
| 151 |
+
|
| 152 |
+
### @yahyaamin2461
|
| 153 |
+
|
| 154 |
+
1 year ago
|
| 155 |
+
|
| 156 |
+
Reading Reading improves our knowledge. It teaches us to use a language properly. It helps to know and learn new things. And that's why we all need to read a lot. The most important way of acquiring knowledge is through reading. One cannot attain education in this world without reading. Hence reading is the part and parcel of one's life. Cultivating reading habits will not only help a student in his academic career, but also help one attain the pinnacle in one's professional career and personal life.
|
| 157 |
+
|
| 158 |
+
Show less Read more
|
| 159 |
+
|
| 160 |
+
Like
|
| 161 |
+
|
| 162 |
+
Dislike
|
| 163 |
+
|
| 164 |
+
Reply
|
| 165 |
+
|
| 166 |
+
Image 18
|
| 167 |
+
|
| 168 |
+
### @d1gardner
|
| 169 |
+
|
| 170 |
+
1 year ago
|
| 171 |
+
|
| 172 |
+
All of a sudden, I cannot change text colors.
|
| 173 |
+
|
| 174 |
+
Show less Read more
|
| 175 |
+
|
| 176 |
+
Like
|
| 177 |
+
|
| 178 |
+
Dislike
|
| 179 |
+
|
| 180 |
+
Reply
|
| 181 |
+
|
| 182 |
+
Top is selected, so you'll see featured comments
|
| 183 |
+
|
| 184 |
+
## Comments 2
|
| 185 |
+
|
| 186 |
+
Top Show featured commentsNewest Show recent comments, including potential spam
|
| 187 |
+
|
| 188 |
+
## In this video
|
| 189 |
+
|
| 190 |
+
Timeline
|
| 191 |
+
|
| 192 |
+
Transcript
|
| 193 |
+
|
| 194 |
+
## Description
|
| 195 |
+
|
| 196 |
+
Libreoffice How To Change Text Color
|
| 197 |
+
|
| 198 |
+
7 Likes
|
| 199 |
+
|
| 200 |
+
3,309 Views
|
| 201 |
+
|
| 202 |
+
2023 Mar 13
|
| 203 |
+
|
| 204 |
+
learn libreoffice basic tutorial for beginner how to change all text color fast and easy #libreoffice#howto#mufasucad…...more
|
| 205 |
+
|
| 206 |
+
...more Show less
|
| 207 |
+
|
| 208 |
+
## Music 1 songs
|
| 209 |
+
|
| 210 |
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Image 19 # Back In The City #### The 126ers Back In The City
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Music
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Follow along using the transcript.
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Show transcript
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Image 20 ### Mufasu CAD 230K subscribers
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VideosAboutImage 21 FacebookImage 22 LinkedInImage 23 Twitter
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Image 24: true 3:13 3:13Gimp How To Bend Text by Mufasu CAD
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Image 25: true 1:39 1:39Solid Edge How To Delete Relationships by Mufasu CAD
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Image 26: true 1:10 1:10Blender How To Export UV Map by Mufasu CAD
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## Transcript
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NaN / NaN
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Image 27 LIVE
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### Livestream, "Good Luck Charm" Lucky Crane Couple, Screensaver.TV Picture Art.
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Get Inspired By Art
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2K watching
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Image 29 8:21
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Image 30
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### Impress (20) – Fontwork
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MichaelsTechTutorials
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Image 32
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### Storchennest Live Webcam in Bad Salzungen, Thüringen
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Stadtverwaltung Bad Salzungen
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2K watching
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Image 33 1:42
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Image 34
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### How to Change Font Color in LibreOffice
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MrQuick
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1.4K views • 1 year ago
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Image 35 3:00:01
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### 3-Hour Serene Yellow & Orange Gradient - Uplift and Relax Your Space
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ONCE REELED
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418K views • 2 years ago
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Image 37 2:00:18
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Image 38
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### Baby Blue Aura | Background | To relax | HD 4K
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aesthetic spirit
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28K views • 4 months ago
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### 2 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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### pantalla a100 aro luz blanco
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### Pink Hue Wallpaper Screen
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Seraphic Glow
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### Moody Gardens Penguin Cam LIVE | Penguin Habitat Stream at the Aquarium in Galveston, Texas
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Moody Gardens
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985 watching
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Image 47 3:00:53
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### Light aura background light
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𝐢𝐫𝐞ꨄ
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MG1010
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### Pink Ombre Aura Screen | 3 Hours and 1 Second | No Sound
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Set the Mood
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Image 53 3:00:11
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### 3 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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683K views • 2 years ago
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### Gradient Liquid Blue Shapes Animation Background video | Footage | Screensaver
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MG1010
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### Vintage Floral Free Tv Art Wallpaper Screensaver Home Decor Samsung Oil Painting Digital Wildflower
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LunaSkyeTvArt
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602K views • 2 years ago
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Image 59 3:01:41
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Image 60
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### Night Light Beige Screen 3 Hours No Ads #ledlights #colors #mood #beige #chromakey #nosound #asmr
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Night Light HD
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13K views • 2 years ago
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Image 62
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### Vintage oil paintings for Frame TV | Samsung TV | Smart TV art | screensaver | black & white flowers
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Frame TV Art
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Image 64
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### Vibrant Yellow Flower Oil Painting for TV | Frame Art TV Screensaver | 4K Visuals | 2 Hours
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Frame TV Art Gallery Wallpapers
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3.9K views • 1 year ago
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Image 65 2:00:00
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Image 66
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### Vintage White Peonies Painting | Gold Frame TV Art | Art Screensaver for TV | 1 Scene - 2 Hrs
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Interactive Gallery
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4.8M views • 2 years ago
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Show more
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Join this channel and unlock members-only perks
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/04_libreoffice-how-to-change-text-color/images.json
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| 1 |
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{
|
| 2 |
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"title": "How To Change The Default Slide Color In LibreOffice ...",
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| 3 |
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"link": "https://www.reddit.com/r/techtalktutorials/comments/1m65hcq/how_to_change_the_default_slide_color_in/",
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| 4 |
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"snippet": "Open your project in LibreOffice Impress. · Select the slide that you want to change the background color of. · Click on the \"Tools\" tab and ...",
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/06_changing-color-of-characters-and-background-in-text-box/article_markdown.md
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|
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| 1 |
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Title: Oh noes!
|
| 2 |
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|
| 3 |
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URL Source:
|
| 4 |
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|
| 5 |
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Warning: Target URL returned error 500: Internal Server Error
|
| 6 |
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|
| 7 |
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Markdown Content:
|
| 8 |
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Image 1: Sad Anubis
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| 9 |
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|
| 10 |
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Internal Server Error: administrator has misconfigured Anubis. Please contact the administrator and ask them to look for the logs around: double_spend.
|
| 11 |
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|
| 12 |
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Go home
|
articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/06_changing-color-of-characters-and-background-in-text-box/images.json
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|
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| 10 |
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| 11 |
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"snippet": "On the area tab, select color and pick wanted color. For the text, again with the text box selected, select the text you want to change color. Then right mouse ...",
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/06_changing-color-of-characters-and-background-in-text-box/serper_result.json
ADDED
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@@ -0,0 +1,7 @@
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| 1 |
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{
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"title": "Changing color of characters and background in text box",
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| 3 |
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"link": "https://ask.libreoffice.org/t/changing-color-of-characters-and-background-in-text-box/73124",
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"date": "Jan 23, 2022",
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/07_how-to-change-font-color-and-other-effects/article_markdown.md
ADDED
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Title: How to Change Font Color (and Other Effects)
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URL Source:
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Markdown Content:
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# How to Change Font Color (and Other Effects) - YouTube
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Back Image 1: Meet the music reviewer community
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Skip navigation
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Search
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Search with your voice
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[](
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Image 2: Meet the music reviewer community
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[](
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Video 2
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How to Change Font Color (and Other Effects)
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Tap to unmute
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Image 3
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## How to Change Font Color (and Other Effects)
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Free Office App Galore 1,320 views 2 years ago
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Copy link
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Info
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Shopping
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Image 4
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Image 5
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If playback doesn't begin shortly, try restarting your device.
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You're signed out
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Videos you watch may be added to the TV's watch history and influence TV recommendations. To avoid this, cancel and sign in to YouTube on your computer.
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Cancel Confirm
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Share
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[]( "Share link")- [x] Include playlist
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An error occurred while retrieving sharing information. Please try again later.
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Image 6
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0:00
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[]( "Next (SHIFT+n)")
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0:00 / 0:00
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Live
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•Watch full video
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Intro
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2:11 How to Add More Templates Free Office App Galore 1.3K views • 2 years ago Live Playlist ()Mix (50+)9:22 Optimizing LibreOffice To Work Like Microsoft Office ...Michael Horn 363K views • 1 year ago Live Playlist ()Mix (50+)9:17 Impress(29) - Interaction MichaelsTechTutorials 7.8K views • 8 years ago Live Playlist ()Mix (50+)Livestream, "Good Luck Charm" Lucky Crane Couple, Screensaver.TV Picture Art.Get Inspired By Art 1.9K watching • 1 day ago Live Playlist ()Mix (50+)[7:25 Font Works - Libre Office Writer Tutorial - [ Word Art Tutorial ]DCP Web Designers 14K views • 7 years ago Live Playlist ()Mix (50+)]( Hooters Waitress - SNL Saturday Night Live 8.6M views • 2 years ago Live Playlist ()Mix (50+)5:45 ADHD Child vs. Non-ADHD Child Interview Mental Health Matters 28M views • 10 years ago Live Playlist ()Mix (50+)4:46 Airbnb Superhost - SNL Saturday Night Live 1.8M views • 1 month ago Live Playlist ()Mix (50+)🔴 EN VIVO | Reloj / Hora actual en Ciudad de México / LIVE Mexico City Clock / What time is it now High Dropping 246 watching • 6 months ago Live Playlist ()Mix (50+)2:30:00 2 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV Pigment Peace 65K views • 1 year ago Live Playlist ()Mix (50+)3:00:01 3-Hour Serene Yellow & Orange Gradient - Uplift and Relax Your Space ONCE REELED 418K views • 2 years ago Live Playlist ()Mix (50+)[3:00:00 Purple Screen 3 Hours [4K]HEPPENDI - Every Kind 5.6K views • 3 months ago Live Playlist ()Mix (50+)](
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Sign in to confirm you’re not a bot This helps protect our community. Learn more
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# How to Change Font Color (and Other Effects)
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Image 7: Free Office App Galore
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Free Office App Galore
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Free Office App Galore
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88 subscribers
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Subscribed
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Share
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Save
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Download
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Download
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1.3K views 2 years ago
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1,320 views • Jul 4, 2023
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Download for FREE: "194 LibreOffice Shortcuts + Quick Reference Guide" here: In this video, I will show you, how to change font color and apply some other effects on your text in LibreOffice Impress.…...more
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...more
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## Chapters
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View all
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Image 8 #### Intro #### Intro 0:00
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#### Intro
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0:00
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Image 9 #### Change Font Color #### Change Font Color 0:12
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#### Change Font Color
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0:12
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Image 10 #### Text Decoration #### Text Decoration 0:41
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#### Text Decoration
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0:41
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Image 11 #### Conclusion #### Conclusion 1:11
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#### Conclusion
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1:11
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Transcript
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Follow along using the transcript.
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Show transcript
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Image 12 ### Free Office App Galore 88 subscribers
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VideosAbout
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Show less
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# How to Change Font Color (and Other Effects)
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1,320 views 1.3K views
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Jul 4, 2023
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Save
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## 5 Comments
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Sort comments
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Sort by
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Top Show featured commentsNewest Show recent comments, including potential spam
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Image 13: Default profile photo
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Add a comment...
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Image 14
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### @thecongregationofgracechur8667
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6 months ago
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Is there a way to customize more text effects like the size, color, etc. of shadow/outline of letters? I'm having trouble finding how to make the black outline of my white text thicker or how to make the shadow distance closer and a different color. Thanks.
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Show less Read more
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Hide replies
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Image 17: Free Office App Galore
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1 reply
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Image 18
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### @billarthur453
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1 year ago
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Hi Can you advise how to get two coloured text in libreoffice impress
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Show less Read more
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Image 19
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Image 20
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Hide replies
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Image 21: Free Office App Galore
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Top is selected, so you'll see featured comments
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## Comments 5
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Top Show featured commentsNewest Show recent comments, including potential spam
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## In this video
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Chapters
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Transcript
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## Chapters
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These chapters are auto-generatedImage 22 #### Intro #### Intro 0:00
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#### Intro
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Image 23 #### Change Font Color #### Change Font Color 0:12
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#### Change Font Color
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0:12
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Image 24 #### Text Decoration #### Text Decoration 0:41
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#### Text Decoration
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0:41
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Image 25 #### Conclusion #### Conclusion 1:11
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#### Conclusion
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1:11
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Sync to video time
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## Description
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How to Change Font Color (and Other Effects)
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9 Likes
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1,320 Views
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2023 Jul 4
|
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|
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Download for FREE: "194 LibreOffice Shortcuts + Quick Reference Guide" here: In this video, I will show you, how to change font color and apply some other effects on your text in LibreOffice Impress. Helpful Links: If you are tired of LibreOffice's outdated looks, try this:
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...more Show less
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View all
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Image 26 #### Intro #### Intro 0:00
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Image 27 #### Change Font Color #### Change Font Color 0:12
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Image 28 #### Text Decoration #### Text Decoration 0:41
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Image 29 #### Conclusion #### Conclusion 1:11
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Follow along using the transcript.
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Show transcript
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Image 30 ### Free Office App Galore 88 subscribers
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VideosAbout
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## Transcript
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NaN / NaN
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Image 31 2:11
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Image 32
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### How to Add More Templates
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1.3K views • 2 years ago
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Image 33 9:22
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Image 34
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### Optimizing LibreOffice To Work Like Microsoft Office ...
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Michael Horn
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363K views • 1 year ago
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Image 35 9:17
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Image 36
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### Impress(29) - Interaction
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MichaelsTechTutorials
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7.8K views • 8 years ago
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Image 37 LIVE
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Image 38
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### Livestream, "Good Luck Charm" Lucky Crane Couple, Screensaver.TV Picture Art.
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Get Inspired By Art
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1.9K watching
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Image 39 7:25
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Image 40
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### [Font Works - Libre Office Writer Tutorial - [ Word Art Tutorial ]](
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DCP Web Designers
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14K views • 7 years ago
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Image 41 4:18
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Image 42
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### Hooters Waitress - SNL
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Saturday Night Live
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8.6M views • 2 years ago
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Image 43 5:45
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Image 44
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### ADHD Child vs. Non-ADHD Child Interview
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Mental Health Matters
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28M views • 10 years ago
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Image 45 4:46
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Image 46
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### Airbnb Superhost - SNL
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Saturday Night Live
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1.8M views • 1 month ago
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Image 47 LIVE
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Image 48
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### 🔴 EN VIVO | Reloj / Hora actual en Ciudad de México / LIVE Mexico City Clock / What time is it now
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High Dropping
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246 watching
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Image 49 2:30:00
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Image 50
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### 2 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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65K views • 1 year ago
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Image 51 3:00:01
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Image 52
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### 3-Hour Serene Yellow & Orange Gradient - Uplift and Relax Your Space
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ONCE REELED
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418K views • 2 years ago
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Image 53 3:00:00
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### [Purple Screen 3 Hours [4K]](
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HEPPENDI - Every Kind
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5.6K views • 3 months ago
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Image 55 1:00:04
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### pantalla a100 aro luz blanco
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Milton Cubillos
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### Pink Ombre Aura Screen | 3 Hours and 1 Second | No Sound
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Set the Mood
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2.6M views • 2 years ago
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Image 59 1:00:04
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### Navajo White Screen 1 Hour 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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88K views • 2 years ago
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Image 61 3:00:01
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### Pink Hue Wallpaper Screen
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Seraphic Glow
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472K views • 2 years ago
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Image 63 2:00:00
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### Vintage Floral Free Tv Art Wallpaper Screensaver Home Decor Samsung Oil Painting Digital Wildflower
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LunaSkyeTvArt
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602K views • 2 years ago
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Image 65 3:00:53
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Image 66
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### Light aura background light
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𝐢𝐫𝐞ꨄ
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143K views • 2 months ago
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Image 67 2:00:01
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/07_how-to-change-font-color-and-other-effects/images.json
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|
| 1 |
+
Title: How to Change Text Color in LibreOffice Impress 2025 (Fast & Easy Guide)
|
| 2 |
+
|
| 3 |
+
URL Source:
|
| 4 |
+
|
| 5 |
+
Markdown Content:
|
| 6 |
+
# How to Change Text Color in LibreOffice Impress 2025 (Fast & Easy Guide) - YouTube
|
| 7 |
+
|
| 8 |
+
Back Image 1: Meet the music reviewer community
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| 9 |
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Skip navigation
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Search
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Search with your voice
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| 15 |
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| 16 |
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[](
|
| 17 |
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|
| 18 |
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Image 2: Meet the music reviewer community
|
| 19 |
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|
| 20 |
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[](
|
| 21 |
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|
| 22 |
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Video 2
|
| 23 |
+
|
| 24 |
+
How to Change Text Color in LibreOffice Impress 2025 (Fast & Easy Guide)
|
| 25 |
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|
| 26 |
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Tap to unmute
|
| 27 |
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|
| 28 |
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Image 3
|
| 29 |
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|
| 30 |
+
## How to Change Text Color in LibreOffice Impress 2025 (Fast & Easy Guide)
|
| 31 |
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|
| 32 |
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WebCraft 131 views 1 year ago
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Copy link
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8:00 1 IN A MILLION MOMENTS IN SPORTS !Hyperloop 3.7M • 4y ago Live Playlist ()Mix (50+)3:00:01 3-Hour Serene Yellow & Orange Gradient - Uplift and Relax Your Space ONCE REELED 418K • 2y ago Live Playlist ()Mix (50+)1:00:01 Bright Flying Green Fire Sparks Background video | Footage | Screensaver MG1010 313K • 5y ago Live Playlist ()Mix (50+)3:00:01 Vintage While Panting | Screensaver for TV Wallpaper #tvwallpaper #tv #vintagepainting #frame #glod Amazing Avira 2.6K • 10mo ago Live Playlist ()Mix (50+)3:00:53 Light aura background light 𝐢𝐫𝐞ꨄ 143K • 2mo ago Live Playlist ()Mix (50+)1:00:04 pantalla a100 aro luz blanco Milton Cubillos 1.2M • 5y ago Live Playlist ()Mix (50+)3:00:01 Pink Ombre Aura Screen | 3 Hours and 1 Second | No Sound Set the Mood 2.6M • 2y ago Live Playlist ()Mix (50+)3:22:01 Roses Vase Painting | Gold Frame TV Art | Art Screensaver for TV 3 Hrs FRAME TV ART 731 • 1y ago Live Playlist ()Mix (50+)1:00:04 Navajo White Screen 1 Hour 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV Pigment Peace 88K • 2y ago Live Playlist ()Mix (50+)2:00:01 Vibrant Yellow Flower Oil Painting for TV | Frame Art TV Screensaver | 4K Visuals | 2 Hours Frame TV Art Gallery Wallpapers 3.9K • 1y ago Live Playlist ()Mix (50+)1:00:00 sunset aura 🌄| focus background wallpaper for studying | the aesthetic guide 𝑻𝒉𝒆 𝑨𝒆𝒔𝒕𝒉𝒆𝒕𝒊𝒄 𝑮𝒖𝒊𝒅𝒆 ♥785K • 1y ago Live Playlist ()Mix (50+)1:00:04 Softbox Lighting | Softbox Light | Photography Lighting Useful Tools 237K • 5y ago Live Playlist ()Mix (50+)
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# How to Change Text Color in LibreOffice Impress 2025 (Fast & Easy Guide)
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Image 7
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WebCraft
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WebCraft
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1.13K subscribers
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131 views 1 year ago#PresentationDesign#LibreOffice2025#ChangeTextColor
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131 views • May 9, 2025 • #PresentationDesign #LibreOffice2025 #ChangeTextColor
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Learn to change text color in LibreOffice Impress 2025 for vibrant presentations, slideshows, or branding materials—perfect for educators, marketers, or designers boosting visual appeal, emphasizing key points, or aligning with brand guidelines without tedious manual adjustments!…...more
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...more
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Transcript
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Follow along using the transcript.
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Show transcript
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Image 8 ### WebCraft 1.13K subscribers
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VideosAbout
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Show less
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# How to Change Text Color in LibreOffice Impress 2025 (Fast & Easy Guide)
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131 views 131 views
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May 9, 2025
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Save
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## 1 Comment
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Sort by
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Top Show featured commentsNewest Show recent comments, including potential spam
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Image 9: Default profile photo
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Add a comment...
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Image 10
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### @ericevans1196
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3 months ago
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Thank you for helping me
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Top is selected, so you'll see featured comments
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## Comments 1
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Top Show featured commentsNewest Show recent comments, including potential spam
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## In this video
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Transcript
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## Description
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How to Change Text Color in LibreOffice Impress 2025 (Fast & Easy Guide)
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2 Likes
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131 Views
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2025 May 9
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Learn to change text color in LibreOffice Impress 2025 for vibrant presentations, slideshows, or branding materials—perfect for educators, marketers, or designers boosting visual appeal, emphasizing key points, or aligning with brand guidelines without tedious manual adjustments! SEO Keywords: "change text color LibreOffice Impress 2025," "customize font color tutorial," "text styling guide," "fix color consistency issues," "how to modify text color in slides," "presentation design tips," "Impress 2025 formatting," "branding color adjustments" Need Help? Email devine2526@gmail.com for personalized support. Hashtags: #LibreOffice2025#ChangeTextColor#PresentationDesign#DesignerTips#TextColorTutorial…...more
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...more Show less
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Follow along using the transcript.
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Show transcript
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Image 11 ### WebCraft 1.13K subscribers
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VideosAbout
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## Transcript
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NaN / NaN
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Image 12 8:00
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Image 13
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### 1 IN A MILLION MOMENTS IN SPORTS !
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Hyperloop
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3.7M 4y ago
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Image 14 3:00:01
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Image 15
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### 3-Hour Serene Yellow & Orange Gradient - Uplift and Relax Your Space
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ONCE REELED
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418K 2y ago
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Image 16 1:00:01
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Image 17
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### Bright Flying Green Fire Sparks Background video | Footage | Screensaver
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MG1010
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313K 5y ago
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Image 18 3:00:01
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Image 19
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### Vintage While Panting | Screensaver for TV Wallpaper #tvwallpaper #tv #vintagepainting #frame #glod
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Amazing Avira
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2.6K 10mo ago
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Image 20 3:00:53
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Image 21
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### Light aura background light
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𝐢𝐫𝐞ꨄ
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143K 2mo ago
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Image 22 1:00:04
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Image 23
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### pantalla a100 aro luz blanco
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Milton Cubillos
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1.2M 5y ago
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Image 24 3:00:01
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Image 25
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### Pink Ombre Aura Screen | 3 Hours and 1 Second | No Sound
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Set the Mood
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2.6M 2y ago
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Image 26 3:22:01
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Image 27
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### Roses Vase Painting | Gold Frame TV Art | Art Screensaver for TV 3 Hrs
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FRAME TV ART
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731 1y ago
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Image 28 1:00:04
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Image 29
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### Navajo White Screen 1 Hour 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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88K 2y ago
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Image 31
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### Vibrant Yellow Flower Oil Painting for TV | Frame Art TV Screensaver | 4K Visuals | 2 Hours
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Frame TV Art Gallery Wallpapers
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3.9K 1y ago
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Image 32 1:00:00
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Image 33
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### sunset aura 🌄| focus background wallpaper for studying | the aesthetic guide
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𝑻𝒉𝒆 𝑨𝒆𝒔𝒕𝒉𝒆𝒕𝒊𝒄 𝑮𝒖𝒊𝒅𝒆 ♥
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785K 1y ago
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Image 34 1:00:04
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Image 35
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### Softbox Lighting | Softbox Light | Photography Lighting
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Useful Tools
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237K 5y ago
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Image 36 2:00:00
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Image 37
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### Vintage Floral Free Tv Art Wallpaper Screensaver Home Decor Samsung Oil Painting Digital Wildflower
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LunaSkyeTvArt
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602K 2y ago
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Image 38 1:02:37
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Image 39
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### 1 hour blue aura background
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Calming Backgrounds
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150K 10mo ago
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Image 40 3:00:01
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Image 41
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### Pastel Pink Screen | 3 Hours and 1 Second | No Sound
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Set the Mood
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433K 2y ago
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Image 42 2:00:04
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Image 43
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### A Floral Dream in Pastel Tones| Dreamy Spring Floral Art for Frame TV | 4K Screensaver
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VintageView Channel
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2.6K 1mo ago
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Image 44 1:00:00
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Image 45
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### Rounded Neon Multicolored lines Animation Background Video | Footage | Screensaver
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MG1010
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9M 7y ago
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Image 46 2:30:00
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Image 47
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### 2 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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65K 1y ago
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Image 48 3:00:01
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Image 49
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### Pink Hue Wallpaper Screen
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Seraphic Glow
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472K 2y ago
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Image 50 3:00:11
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Image 51
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### Morning Garden Serenity, Impressionist Oil Painting | Vintage Frame Art Screensaver for TV
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Flowers Artwork
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9.9K 10mo ago
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Show more
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How are your recommendations today?
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|
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Not applicable Bad Fair Good Very good Extremely good
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/08_how-to-change-text-color-in-libreoffice-impress-2025/images.json
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| 1 |
+
Title: How to Change Font Color in LibreOffice
|
| 2 |
+
|
| 3 |
+
URL Source:
|
| 4 |
+
|
| 5 |
+
Markdown Content:
|
| 6 |
+
# How to Change Font Color in LibreOffice - YouTube
|
| 7 |
+
|
| 8 |
+
Back Image 1: Meet the music reviewer community
|
| 9 |
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| 10 |
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Skip navigation
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| 11 |
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| 12 |
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Search
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Search with your voice
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[](
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| 17 |
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| 18 |
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Image 2: Meet the music reviewer community
|
| 19 |
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| 20 |
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[](
|
| 21 |
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|
| 22 |
+
Video 2
|
| 23 |
+
|
| 24 |
+
How to Change Font Color in LibreOffice
|
| 25 |
+
|
| 26 |
+
Tap to unmute
|
| 27 |
+
|
| 28 |
+
Image 3
|
| 29 |
+
|
| 30 |
+
## How to Change Font Color in LibreOffice
|
| 31 |
+
|
| 32 |
+
MrQuick 1,470 views 1 year ago
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| 33 |
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| 34 |
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Copy link
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| 35 |
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| 36 |
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Info
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| 37 |
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| 38 |
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Shopping
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| 39 |
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| 40 |
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Image 4
|
| 41 |
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|
| 42 |
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Image 5
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| 43 |
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| 44 |
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If playback doesn't begin shortly, try restarting your device.
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| 45 |
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Videos you watch may be added to the TV's watch history and influence TV recommendations. To avoid this, cancel and sign in to YouTube on your computer.
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Cancel Confirm
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Share
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[]( "Share link")- [x] Include playlist
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Image 6
|
| 59 |
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| 60 |
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0:00
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| 61 |
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| 62 |
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[]( "Next (SHIFT+n)")
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| 64 |
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0:00 / 0:00
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Live
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•Watch full video
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| 70 |
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Whats in Video
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| 71 |
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| 72 |
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1:04 How to Delete Table in Libreoffice Writer MrQuick 1.3K views • 1 year ago Live Playlist ()Mix (50+)F.03 Livestream - Day 4 Figure 1.5K watching • 3 days ago Live Playlist ()Mix (50+)Storchennest Live Webcam in Bad Salzungen, Thüringen Stadtverwaltung Bad Salzungen 2K watching • 3 months ago Live Playlist ()Mix (50+)Livestream, "Good Luck Charm" Lucky Crane Couple, Screensaver.TV Picture Art.Get Inspired By Art 2K watching • 1 day ago Live Playlist ()Mix (50+)Femern Livestream FemernAS 288 watching • 2 weeks ago Live Playlist ()Mix (50+)Moody Gardens Penguin Cam LIVE | Penguin Habitat Stream at the Aquarium in Galveston, Texas Moody Gardens 983 watching • 1 month ago Live Playlist ()Mix (50+)2:28:20 God Says:"I’M WAITING FOR YOU — DON’T MISS THIS TODAY"/God Message Now/God Message God's Love Unending 17K views • 11 hours ago Live Playlist ()Mix (50+)3:00:01 3-Hour Serene Yellow & Orange Gradient - Uplift and Relax Your Space ONCE REELED 418K views • 2 years ago Live Playlist ()Mix (50+)2:00:18 Baby Blue Aura | Background | To relax | HD 4K aesthetic spirit 28K views • 4 months ago Live Playlist ()Mix (50+)3:00:11 3 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV Pigment Peace 683K views • 2 years ago Live Playlist ()Mix (50+)[2:57:01 PURPLE & PINK GRADIENT IN HD [3 HOURS]Colors in focus 298K views • 2 years ago Live Playlist ()Mix (50+)]( Pink Ombre Aura Screen | 3 Hours and 1 Second | No Sound Set the Mood 2.6M views • 2 years ago Live Playlist ()Mix (50+)
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| 73 |
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| 74 |
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Sign in to confirm you’re not a bot This helps protect our community. Learn more
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| 76 |
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# How to Change Font Color in LibreOffice
|
| 77 |
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|
| 78 |
+
Image 7
|
| 79 |
+
|
| 80 |
+
MrQuick
|
| 81 |
+
|
| 82 |
+
MrQuick
|
| 83 |
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| 84 |
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29K subscribers
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| 85 |
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| 86 |
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Join
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| 87 |
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| 88 |
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Subscribed
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| 89 |
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| 90 |
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Share
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| 91 |
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| 92 |
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Save
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| 93 |
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| 94 |
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Download
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| 96 |
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Download
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| 97 |
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| 98 |
+
1.4K views 1 year ago 2 products
|
| 99 |
+
|
| 100 |
+
1,470 views • Mar 12, 2025 • 2 products
|
| 101 |
+
|
| 102 |
+
How to Change Font Color in LibreOffice Writer using the Character option is shown in this LibreOffice Tutorial. If you are editing your business or legal document or even Academic document you many need to change the font color. The video addresses multiple ways to change font color …...more
|
| 103 |
+
|
| 104 |
+
...more
|
| 105 |
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|
| 106 |
+
MrQuick tagged products below.Learn more
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| 107 |
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| 108 |
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Image 8 Libre Office 5.1 Impress, Draw, Base Book- Vol 2 Limited shipping areas Flipkart flipkart.com/libre-office-5-1-impress-draw-base-book-2/p/itmeta3grferkxmy?pid=9798895191156&lid=LSTBOK9798895191156VMBVIO&marketplace=FLIPKART&cmpid=content_book_8965229628_gmc Shop FlipkartImage 9 Libre Office 5.1 Writer, Calc, Math Formula Book- Vol 1 Limited shipping areas Flipkart flipkart.com/libre-office-5-1-writer-calc-math-formula-book-1/p/itmeta3gfakjs8bg?pid=9798895191149&lid=LSTBOK9798895191149FEQR9Y&marketplace=FLIPKART&cmpid=content_book_8965229628_gmc Shop Flipkart
|
| 109 |
+
|
| 110 |
+
How this was made
|
| 111 |
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| 112 |
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Auto-dubbed
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| 113 |
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|
| 114 |
+
Audio tracks for some languages were automatically generated. Learn more
|
| 115 |
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|
| 116 |
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## Chapters
|
| 117 |
+
|
| 118 |
+
View all
|
| 119 |
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|
| 120 |
+
Image 10 #### Whats in Video #### Whats in Video 0:00
|
| 121 |
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|
| 122 |
+
#### Whats in Video
|
| 123 |
+
|
| 124 |
+
0:00
|
| 125 |
+
|
| 126 |
+
Image 11 #### Method 1 #### Method 1 0:11
|
| 127 |
+
|
| 128 |
+
#### Method 1
|
| 129 |
+
|
| 130 |
+
0:11
|
| 131 |
+
|
| 132 |
+
Image 12 #### Method 2 #### Method 2 0:34
|
| 133 |
+
|
| 134 |
+
#### Method 2
|
| 135 |
+
|
| 136 |
+
0:34
|
| 137 |
+
|
| 138 |
+
Image 13 #### Method 3 #### Method 3 0:52
|
| 139 |
+
|
| 140 |
+
#### Method 3
|
| 141 |
+
|
| 142 |
+
0:52
|
| 143 |
+
|
| 144 |
+
Explore this course
|
| 145 |
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|
| 146 |
+
Image 1474 lessons 74 lessons
|
| 147 |
+
|
| 148 |
+
How to use Libreoffice
|
| 149 |
+
|
| 150 |
+
### Course progress
|
| 151 |
+
|
| 152 |
+
0 of 74 lessons complete
|
| 153 |
+
|
| 154 |
+
Transcript
|
| 155 |
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|
| 156 |
+
Follow along using the transcript.
|
| 157 |
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|
| 158 |
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Show transcript
|
| 159 |
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|
| 160 |
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Image 15 ### MrQuick 29K subscribers
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| 161 |
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| 162 |
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VideosAboutImage 16 Discount Stores
|
| 163 |
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| 164 |
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Image 1774 74
|
| 165 |
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|
| 166 |
+
How to use Libreoffice
|
| 167 |
+
|
| 168 |
+
by MrQuick
|
| 169 |
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|
| 170 |
+
Image 18: true 1:36 1:36How to Change Font Size in LibreOffice by MrQuick
|
| 171 |
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|
| 172 |
+
Show less
|
| 173 |
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|
| 174 |
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[]( products
|
| 175 |
+
# How to Change Font Color in LibreOffice
|
| 176 |
+
|
| 177 |
+
1,470 views 1.4K views
|
| 178 |
+
|
| 179 |
+
Mar 12, 2025
|
| 180 |
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|
| 181 |
+
Save
|
| 182 |
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|
| 183 |
+
## Comments
|
| 184 |
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|
| 185 |
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## Comments
|
| 186 |
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|
| 187 |
+
Top Show featured commentsNewest Show recent comments, including potential spam
|
| 188 |
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|
| 189 |
+
## In this video
|
| 190 |
+
|
| 191 |
+
Timeline
|
| 192 |
+
|
| 193 |
+
Chapters
|
| 194 |
+
|
| 195 |
+
Transcript
|
| 196 |
+
|
| 197 |
+
## Chapters
|
| 198 |
+
|
| 199 |
+
Image 19 #### Whats in Video #### Whats in Video 0:00
|
| 200 |
+
|
| 201 |
+
#### Whats in Video
|
| 202 |
+
|
| 203 |
+
Image 20 #### Method 1 #### Method 1 0:11
|
| 204 |
+
|
| 205 |
+
#### Method 1
|
| 206 |
+
|
| 207 |
+
0:11
|
| 208 |
+
|
| 209 |
+
Image 21 #### Method 2 #### Method 2 0:34
|
| 210 |
+
|
| 211 |
+
#### Method 2
|
| 212 |
+
|
| 213 |
+
0:34
|
| 214 |
+
|
| 215 |
+
Image 22 #### Method 3 #### Method 3 0:52
|
| 216 |
+
|
| 217 |
+
#### Method 3
|
| 218 |
+
|
| 219 |
+
0:52
|
| 220 |
+
|
| 221 |
+
Sync to video time
|
| 222 |
+
|
| 223 |
+
## Products
|
| 224 |
+
|
| 225 |
+
MrQuick tagged products below.Learn more
|
| 226 |
+
|
| 227 |
+
Image 23 #### Libre Office 5.1 Impress, Draw, Base Book- Vol 2 Flipkart Limited shipping areas View
|
| 228 |
+
|
| 229 |
+
Image 24 #### Libre Office 5.1 Writer, Calc, Math Formula Book- Vol 1 Flipkart Limited shipping areas View
|
| 230 |
+
|
| 231 |
+
## Products
|
| 232 |
+
|
| 233 |
+
Image 25 #### Libre Office 5.1 Impress, Draw, Base Book- Vol 2 Flipkart Limited shipping areas View
|
| 234 |
+
|
| 235 |
+
Image 26 #### Libre Office 5.1 Writer, Calc, Math Formula Book- Vol 1 Flipkart Limited shipping areas View
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## Description
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How to Change Font Color in LibreOffice
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10 Likes
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1,470 Views
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2025 Mar 12
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Image 27 Libre Office 5.1 Impress, Draw, Base Book- Vol 2 Limited shipping areas Flipkart flipkart.com/libre-office-5-1-impress-draw-base-book-2/p/itmeta3grferkxmy?pid=9798895191156&lid=LSTBOK9798895191156VMBVIO&marketplace=FLIPKART&cmpid=content_book_8965229628_gmc Shop FlipkartImage 28 Libre Office 5.1 Writer, Calc, Math Formula Book- Vol 1 Limited shipping areas Flipkart flipkart.com/libre-office-5-1-writer-calc-math-formula-book-1/p/itmeta3gfakjs8bg?pid=9798895191149&lid=LSTBOK9798895191149FEQR9Y&marketplace=FLIPKART&cmpid=content_book_8965229628_gmc Shop Flipkart
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How to Change Font Color in LibreOffice Writer using the Character option is shown in this LibreOffice Tutorial. If you are editing your business or legal document or even Academic document you many need to change the font color. The video addresses multiple ways to change font color Join this channel to get access to perks: Image 29/@mrquickaz0:00 Whats in Video 0:11 Method 1 0:34 Method 2 0:52 Method 3…...more
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...more Show less
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How this was made
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Auto-dubbed
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Audio tracks for some languages were automatically generated. Learn more
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View all
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Image 30 #### Whats in Video #### Whats in Video 0:00
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Image 31 #### Method 1 #### Method 1 0:11
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Image 32 #### Method 2 #### Method 2 0:34
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Image 33 #### Method 3 #### Method 3 0:52
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Explore this course
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Image 3474 lessons 74 lessons
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### Course progress
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0 of 74 lessons complete
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Follow along using the transcript.
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Show transcript
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Image 35 ### MrQuick 29K subscribers
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VideosAboutImage 36 Discount Stores
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Image 3774 74
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by MrQuick
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Image 38: true 1:36 1:36How to Change Font Size in LibreOffice by MrQuick
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## Transcript
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NaN / NaN
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Image 39 1:04
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Image 40
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### How to Delete Table in Libreoffice Writer
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1.3K views • 1 year ago
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Image 41 LIVE
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Image 42
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### F.03 Livestream - Day 4
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Figure
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Image 43 LIVE
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Image 44
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### Storchennest Live Webcam in Bad Salzungen, Thüringen
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Stadtverwaltung Bad Salzungen
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2K watching
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Image 45 LIVE
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Image 46
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### Livestream, "Good Luck Charm" Lucky Crane Couple, Screensaver.TV Picture Art.
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Get Inspired By Art
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2K watching
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Image 47 LIVE
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Image 48
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### Femern Livestream
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FemernAS
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288 watching
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Image 49 LIVE
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Image 50
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### Moody Gardens Penguin Cam LIVE | Penguin Habitat Stream at the Aquarium in Galveston, Texas
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Moody Gardens
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983 watching
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Image 51 2:28:20
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Image 52
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### God Says:"I’M WAITING FOR YOU — DON’T MISS THIS TODAY"/God Message Now/God Message
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God's Love Unending
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17K views • 11 hours ago
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New
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Image 54
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### 3-Hour Serene Yellow & Orange Gradient - Uplift and Relax Your Space
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ONCE REELED
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418K views • 2 years ago
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Image 56
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### Baby Blue Aura | Background | To relax | HD 4K
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aesthetic spirit
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28K views • 4 months ago
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Image 57 3:00:11
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Image 58
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### 3 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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683K views • 2 years ago
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Image 59 2:57:01
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Image 60
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### [PURPLE & PINK GRADIENT IN HD [3 HOURS]](
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Colors in focus
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298K views • 2 years ago
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Image 61 3:00:01
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Image 62
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### Pink Ombre Aura Screen | 3 Hours and 1 Second | No Sound
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Set the Mood
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Image 64
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### Vibrant Yellow Flower Oil Painting for TV | Frame Art TV Screensaver | 4K Visuals | 2 Hours
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Frame TV Art Gallery Wallpapers
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Image 66
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### Light aura background light
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𝐢𝐫𝐞ꨄ
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143K views • 2 months ago
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Image 67 2:30:00
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Image 68
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### 2 Hours Navajo White Screen 4K | Background | Backdrop | Screensaver | Full HD | Phone, Monitor, TV
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Pigment Peace
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65K views • 1 year ago
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Image 69 1:00:13
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Image 70
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### Gradient Liquid Blue Shapes Animation Background video | Footage | Screensaver
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MG1010
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1.7M views • 5 years ago
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Image 71 3:00:01
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Image 72
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### Pastel Pink Screen | 3 Hours and 1 Second | No Sound
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Set the Mood
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433K views • 2 years ago
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Image 73 2:00:01
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Image 74
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### FIELD OF BLOOMS | FRAMED TV ART WALLPAPER SCREENSAVER BACKGROUND | 2 HRS
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TV Art Frames
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### Wildflower Meadow | Turn Your TV Into Art | Vintage Oil Painting Screensaver | TV Framed Art | 3 Hrs
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Oil & Ambience
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Image 78
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### A Floral Dream in Pastel Tones| Dreamy Spring Floral Art for Frame TV | 4K Screensaver
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VintageView Channel
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2.6K views • 1 month ago
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Show more
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Join this channel and unlock members-only perks
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/09_how-to-change-font-color-in-libreoffice/images.json
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Title: Libreoffice Impress Course
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URL Source:
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Markdown Content:
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# Libreoffice Impress Course - YouTube
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Back Image 2
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Skip navigation
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Search
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Search with your voice
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[](
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Image 3
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Home HomeShorts ShortsSubscriptions SubscriptionsYou You
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Image 4
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Image 5: Play all
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Play all
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# Libreoffice Impress Course
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Image 6
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by Master of Tutorials
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Playlist•31 videos•562 views
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Learn how to design engaging, professional presentations with LibreOffice Impress. Through short, focused lessons, you’ll discover how to build slides that captivate your audience, combine visuals and text effectively, and present your story with confidence. ...more Learn how to design engaging, professional presentations with LibreOffice Impress. Through short, focused lessons, you’ll discover how to build slides that captivate your audience, combine visuals and text effectively, and present your story with confidence. ...more...more
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Play all
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Image 7: true PLAY ALL
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# Libreoffice Impress Course
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31 videos 562 views Last updated on Mar 8, 2026
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Save playlist
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[](
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Shuffle play
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Share
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Learn how to design engaging, professional presentations with LibreOffice Impress. Through short, focused lessons, you’ll discover how to build slides that captivate your audience, combine visuals and text effectively, and present your story with confidence. Start with a Blank Canvas create a new presentation from scratch, adjust slide size, layout, and orientation, apply and customize themes or templates, manage slides — add, delete, duplicate, or hide them. Design that Speaks insert and format text boxes, adjust font color, size, and alignment, use bullet points for structure and clarity, add hyperlinks to connect slides or external content. Visuals that Stand Out insert, crop, and frame images, set background pictures or borders, use zoom tools for precise editing, align elements with the grid for clean layouts. Add Motion and Sound apply transitions between slides, animate text and objects, insert audio or video for more impact, create smooth, dynamic flow throughout your presentation. Make Data Look Good insert and format tables and charts, include formulas and equations, use notes and comments for feedback or reminders. Polish and Export export your slides as PDFs for sharing, convert your presentation to PowerPoint format, design and reuse templates for consistency, refine details to achieve a professional finish. What You’ll Learn By the end of the course, you’ll know how to: build structured, visually engaging presentations, enhance slides with animation, media, and data, present your ideas with clarity and confidence.
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Show more
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Image 8
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Master of Tutorials
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Master of Tutorials
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Image 9: Play all
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Image 10: Play all
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Image 11
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by Master of Tutorials
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Playlist•31 videos•562 views
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Learn how to design engaging, professional presentations with LibreOffice Impress. Through short, focused lessons, you’ll discover how to build slides that captivate your audience, combine visuals and text effectively, and present your story with confidence. ...more Learn how to design engaging, professional presentations with LibreOffice Impress. Through short, focused lessons, you’ll discover how to build slides that captivate your audience, combine visuals and text effectively, and present your story with confidence. ...more...more
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Image 12: true 1:03 1:03 Now playing
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### How to Change Page Size in LibreOffice Impress
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405 views • 6 months ago
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Image 13: true 1:31 1:31 Now playing
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### How to Insert Text Animation in LibreOffice Impress
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889 views • 6 months ago
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Image 14: true 1:02 1:02 Now playing
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### How to Add Border to Image in LibreOffice Impress
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252 views • 6 months ago
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Image 15: true 0:59 0:59 Now playing
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### How to Insert Comment in LibreOffice Impress
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119 views • 6 months ago
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Image 16: true 1:26 1:26 Now playing
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### How to Zoom In and Zoom Out in LibreOffice Impress
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387 views • 6 months ago
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Image 17: true 0:49 0:49 Now playing
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### How to Enable Grid in LibreOffice Impress
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48 views • 6 months ago
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Image 18: true 0:51 0:51 Now playing
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### How to Change Page Orientation in LibreOffice Impress
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108 views • 6 months ago
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Image 19: true 1:27 1:27 Now playing
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### How to Insert a Formula in LibreOffice Impress
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371 views • 6 months ago
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Image 20: true 0:47 0:47 Now playing
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### How to Change Slide Theme in LibreOffice Impress
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1.4K views • 6 months ago
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Image 21: true 2:14 2:14 Now playing
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### How to Insert Charts in LibreOffice Impress
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301 views • 6 months ago
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Image 22: true 1:00 1:00 Now playing
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### How to Insert a Table in LibreOffice Impress
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343 views • 6 months ago
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Image 23: true 0:55 0:55 Now playing
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### How to Change Slide Layout in LibreOffice Impress
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467 views • 6 months ago
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Image 24: true 1:08 1:08 Now playing
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### How to Insert Audio in LibreOffice Impress
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1.6K views • 6 months ago
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Image 25: true 1:04 1:04 Now playing
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### How to Export a Presentation as PDF in LibreOffice Impress
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243 views • 6 months ago
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Image 26: true 1:14 1:14 Now playing
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### How to Convert a LibreOffice Impress presentation to PowerPoint format?
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1K views • 6 months ago
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Image 27: true 2:37 2:37 Now playing
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### How to Create a Presentation in LibreOffice Impress
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2.7K views • 6 months ago
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Image 28: true 2:02 2:02 Now playing
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### How to Crop Image in LibreOffice Impress | 2 Easy Methods
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318 views • 6 months ago
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Image 29: true 1:13 1:13 Now playing
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### How to Add Notes in LibreOffice Impress
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569 views • 6 months ago
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Image 30: true 0:49 0:49 Now playing
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### How to Change Font Color in LibreOffice Impress
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### How to Add a New Slide in LibreOffice Impress
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984 views • 6 months ago
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Image 32: true 0:43 0:43 Now playing
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### How to Delete a Slide in LibreOffice Impress
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186 views • 6 months ago
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Image 33: true 0:48 0:48 Now playing
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### How to Change Font Size in LibreOffice Impress
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473 views • 6 months ago
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### How to Insert Text in LibreOffice Impress
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95 views • 6 months ago
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Image 35: true 0:36 0:36 Now playing
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### How to Insert an Image in LibreOffice Impress
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601 views • 6 months ago
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Image 36: true 1:13 1:13 Now playing
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### How to Insert a Hyperlink in LibreOffice Impress
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1K views • 6 months ago
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Image 37: true 1:04 1:04 Now playing
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### How to Add Transitions in LibreOffice Impress
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Image 38: true 0:46 0:46 Now playing
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### How to Hide a Slide in LibreOffice Impress | Easy Method
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86 views • 6 months ago
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Image 39: true 1:00 1:00 Now playing
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### How to Create a Template in LibreOffice Impress
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1.1K views • 6 months ago
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Image 40: true 1:04 1:04 Now playing
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### How to Add Bullets in LibreOffice Impress | Simple Method
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365 views • 6 months ago
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Image 41: true 1:09 1:09 Now playing
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### How to Insert a Video in LibreOffice Impress
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1.7K views • 6 months ago
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Image 42: true 0:56 0:56 Now playing
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### How to Insert a Background Image in LibreOffice Impress
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863 views • 6 months ago
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Video 2
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Image 43
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Info
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Shopping
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Image 44
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If playback doesn't begin shortly, try restarting your device.
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You're signed out
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Videos you watch may be added to the TV's watch history and influence TV recommendations. To avoid this, cancel and sign in to YouTube on your computer.
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Cancel Confirm
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Share
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[]( "Share link")- [x] Include playlist
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An error occurred while retrieving sharing information. Please try again later.
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Watch later
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Copy link
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Image 46
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0:00
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[]( "Next (SHIFT+n)")
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Live
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•Watch full video
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NaN / NaN
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articles/libreoffice_impress/04578141-1d42-4146-b9cf-6fab4ce5fd74/10_libreoffice-impress-course/images.json
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|
| 1 |
+
Title: 2311.01767v2.pdf
|
| 2 |
+
|
| 3 |
+
URL Source:
|
| 4 |
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|
| 5 |
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Published Time: Wed, 08 Nov 2023 02:41:27 GMT
|
| 6 |
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|
| 7 |
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Number of Pages: 17
|
| 8 |
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|
| 9 |
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Markdown Content:
|
| 10 |
+
# PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion
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Yiduo Guo 1∗
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, Zekai Zhang 1∗
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, Yaobo Liang 2, Dongyan Zhao 1, Nan Duan 21Peking University
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> 2
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Microsoft Research Asia
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yiduo@stu.pku.edu.cn,justinzzk@stu.pku.edu.cn,yaobo.liang@microsoft.com zhaody@pku.edu.cn,nanduan@microsoft.com
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Abstract
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Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural lan-guage tasks and their ability to translate instruc-tions into tool APIs. However, the evaluation of LLMs utilizing complex tools to finish multi-turn, multi-modal instructions in a complex multi-modal environment has not been inves-tigated. To address this gap, we introduce the PowerPoint Task Completion (PPTC) bench-mark to assess LLMs’ ability to create and edit PPT files based on user instructions. It con-tains 279 multi-turn sessions covering diverse topics and hundreds of instructions involving multi-modal operations. We also propose the PPTX-Match Evaluation System that evaluates if LLMs finish the instruction based on the pre-diction file rather than the label API sequence, thus it supports various LLM-generated API sequences. We measure 3 closed LLMs and 6 open-source LLMs. The results show that GPT-4 outperforms other LLMs with 75.1% accuracy in single-turn dialogue testing but faces challenges in completing entire sessions, achieving just 6% session accuracy. We find three main error causes in our benchmark: er-ror accumulation in the multi-turn session, long PPT template processing, and multi-modality perception. These pose great challenges for fu-ture LLM and agent systems. We release the data, code, and evaluation system of PPTC at
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1 Introduction
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Recent evaluation works for Large Language Mod-els (e.g. ChatGPT and GPT-4 (OpenAI, 2023)) fo-cus on their zero-shot/few-shot abilities on basic natural language tasks (Jiao et al., 2023; Zhong et al., 2023; Wang et al., 2023b; Qin et al., 2023a) and their tool-use ability to generate APIs for solv-ing user instructions, such as basic APIs like a calculator in tool transformer (Schick et al., 2023),
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> *Equal contribution
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Figure 1: Within our benchmark, we simulate this multi-turn dialogue scenario between humans and LLMs to evaluate LLMs’ PPT task completion performance.
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RapidAPIs in ToolLLM (Qin et al., 2023c), and hugggingface APIs in Gorilla (Patil et al., 2023). However, these tool-use works emphasize the trans-lation of natural language instructions into APIs and ignore the challenge of using APIs in the ob-servation of complex multi-modal environments to finish user instructions. Also, their evaluation approach focuses on comparing the generated APIs with the label API sequence, assuming there’s only one unique solution. This approach becomes im-practicable in situations with multiple/unlimited correct solutions. To address these challenges, we introduce Power-Point Task Completion (PPTC), a benchmark that measures LLMs’ performance in creating and editing PPT file tasks based on user instructions. We choose PowerPoint as it includes various elements like textbox, table, and image and supports a wider range of APIs than Word and Ex-cel. Our benchmark has three distinctive features
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> arXiv:2311.01767v2 [cs.CL] 7 Nov 2023 Figure 2: We illustrate how LLMs complete one turn in a session. (A) To prompt the LLM, we provide it with the current instruction, previous instructions (dialogue history), PPT file content, and the API reference file. ’PPT reader’ is a function that transforms the PPT file into the text-based format as the PPT file content. (B) The LLM then generates the API sequence and executes it to obtain the prediction PPT file. (C) We evaluate attributes and position relations in the prediction file.
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from other task completion benchmarks: (1) Multi-turn dialogue with varying difficulty . Our PPTC benchmark simulates the multi-turn dialogue ses-sion between the user and the LLM (see Figure 1) and contains 279 multi-turn sessions. Each multi-turn session in our benchmark includes 2 to 17 turns. Each turn consists of a user instruction that describes the user’s needs, a feasible solution that provides the correct solution, and the resulting label output file. Some turns can be easily addressed us-ing a single API, while over half of the instructions require multiple APIs for completion. We provide the LLM with a reference API file that contains all feasible APIs for selection. (2) Multi-modality .Finishing the instruction of our benchmark requires understanding the multi-modal PPT file content and using multi-modal API operations (e.g., PPTC has 268 image operation-related instructions). (3)
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Evaluation based on the final status : We propose the PPTX-Match Evaluation system to evaluate the LLM’s outcome. To identify if the LLM completes the instruction, it checks the PPT file produced by executing the LLM-generated APIs rather than the LLM-generated APIs, thus all API sequences that lead to the correct final status are acceptable. To finish the instruction, we use the current in-struction, past turns’ instructions (dialogue history), the PPT file content (specific environment informa-tion), and the reference API file as the input to prompt the LLM to generate an API sequence as the solution (See Figure 2 (A)). Then we use the API executor to execute the API sequence and re-turn the user the resulting PPT file (See Figure 2 (B)). We name the resulting PPT file as the predic-tion file. In the evaluation step (See Figure 2 (C)), the PPTX-Match Evaluation system first uses the Python-PPTX library to extract all attributes from the prediction PPT file and the label output file. Then it uses the position relation checker to check if objects’ positions conform to the label relation and the attribute content checker to check if the at-tribute’s content is matched with the corresponding label attribute’s content. The LLM correctly com-pletes the current turn’s instruction if all attributes of the file pass these tests. Evaluation metrics in-clude turn-based accuracy which is the ratio of correctly completed turns to the total number of turns and session-based accuracy which is the ra-tio of correctly completed sessions to the overall session count. We measure the performance of three closed-source LLMs (GPT-4, ChatGPT, and Davince-003) and six open-source LLMs (e.g., LLaMa-2) in our benchmark. We further test planning (e.g., CoT (Wei et al., 2022)) and content selection al-gorithms’ performance based on GPT-4. Experi-ment results show that GPT-4 is the strongest LLM among all LLMs but still encounters challenges when completing entire multi-turn sessions. For ex-ample, although GPT-4 achieves 75.1% turn-based accuracy in the creating new PPT file task, it only achieves 22.7% session-based accuracy as errors made in previous turns. GPT-4 and other LLMs also struggle to process long PPT templates (com-plex file environment). For example, GPT-4 only achieves 38.1% turn-based accuracy in the edit-ing task. We further find that GPT-4 struggles to finish instructions involving non-text modality op-erations, especially for position-related operations, such as ’Put object A on the top of the slide’. It only achieves 24% accuracy in these instructions. In summary, this paper has the following contri-butions: (1) We propose the PowerPoint Task Completion benchmark to measure LLM’s task completion per-formance within the PowerPoint official software. This benchmark contains 279 multi-turn sessions with hundreds of multi-modal instructions in the complex multi-modal environment. (2) We propose the PPTX-evaluation system to automatically measure LLMs’ performance in our benchmark. We test 3 closed-source LLMs and 6 open-source LLMs and find that GPT-4 is the strongest LLM among all LLMs. (3) We further analyze LLMs in our benchmarks and find three key error factors: error accumulation in the session, long PPT template processing, and multi-modality perception. These findings pose significant challenges for future LLMs and LLM-based systems.
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2 PPTC Benchmark
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In this section, we introduce our Power-Point Task Completion (PPTC) benchmark, including the overview of our benchmark, its collection and validation process, and the PPTX-Match Evalua-tion System for evaluation. We further analyze the statistics information of our benchmark.
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2.1 Benchmark Overview Benchmark components Our benchmark focuses on two basic tasks within PowerPoint: creating the new PPT file and editing the existing long PPT template for measuring long PPT Content under-standing. We have gathered 229 multi-turn dia-logue sessions for creating the new PPT file and 50 sessions for editing existing templates. Each multi-turn session includes 2 to 17 turns. Each turn comprises three parts: (1) the user instruction (2) the label output file as the ground truth (3) one feasi-ble API sequence for finishing the instruction. Our benchmark also contains an API reference file that includes 49 feasible APIs for various operations and can complete all instructions in our benchmark. For each API, we describe its functionality and arguments and provide usage guidelines. For com-plex APIs, we also offer example cases. We list the details of all APIs in Appendix A.
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Task description To complete the instruction in one turn, in general, the AI assistant must compre-hend the user’s current and prior instructions for context. It should also analyze the content of the PPT file to identify relevant objects mentioned in the instruction. Additionally, it needs to select ap-propriate APIs from a reference API file to achieve the user’s goals. So we use these as the input of the AI assistant and it should output an API sequence as the solution. Then, it executes this API sequence and provides the user with the resulting PPT file as its response (See the whole process in Figure 2).
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Addressing LLM limitations in our bench-mark Compared to the general AI assistant, LLMs still have two limitations for completing the task in our benchmarks: (1) LLMs can not directly pro-cess the PPT file. So we provide a PPT reader function that extracts all shapes and their informa-tion from the PPT file and transforms them into the text format as the PPT file content. Then LLMs can understand and process the PPT file content. (2) LLMs cannot directly use PPT software through a keyboard and mouse. Therefore, we have defined PPT APIs based on the operational logic within the PPT software. and provide an implementation for these APIs in Python that can swiftly gener-ate PPT files. In future work, it may be possible to explore the use of large multimodal models to understand on-screen content and implement APIs using a keyboard and mouse.
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2.2 Benchmark Collection Design Principles We follow these principles to design our benchmark: (1) Multi-turn instructions: One session in our benchmark should contain multi-turn instructions to finish the user’s complex need. (2) Instructions of varying difficulty: Some instruc-tions can be achieved with a single API, while others necessitate a sequence of APIs for success-ful completion. (3) Diverse multimodal operations: User instructions should cover a wide range of op-erations on PPT, such as text-related, image-related, and position-related APIs. (4) Topic Consistency: The dialogue in a session should center around the session topic. Each user instruction in a session aligns closely with the previous instructions (the context), ensuring a coherent and contextually rel-evant dialogue flow. (5) Practicability First: The session topic and specific instructions should simu-late the user’s need in real world
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Benchmark Collection and Validation To col-lect user instructions, we engage 6 skilled crowd workers who craft instructions in accordance with the principles we’ve outlined. Our crowd workers comprise professional data science engineers well-versed in PowerPoint. To achieve practicability first, we request crowd workers to write instruc-tions based on their actual PowerPoint experience. On each session, the workers are asked to first find and list a practicable session topic. For the edit-ing PPT template task, the topic must based on the template file background and is practicable to the template *. To achieve multi-instructions and topic consistency, the workers write instructions step by step and make them consistent with the topic. To achieve diverse multi-operations, we ask them not to write session that only involves a single modality operation. Each worker takes on a specific role in the instructional writing work and is encouraged to write instructions in his/her own words. Workers were asked to spend at least 20 minutes on every session. We delete repeated sessions and short ses-sions that have no more than 50 tokens. Then we ask the seventh worker to write the
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> *We collect 50 PPT templates from the SlidesCarni-val website ( ). SlidesCarnival is a free and open-source PPT template web-site. Each session in the editing task has a unique template. We encourage topic diversity in our templates. We remove templates that are too short (2 ∼5 slides) and have repeated topics.
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feasible API sequence with minimal API usage for each instruction. Next, the workers create the PPT label file by using the provided API sequence. During the whole process, the principal engineer reviews and refines the instructions and API se-quences written by the above 7 workers for initial quality assurance. To ensure the data quality of this benchmark, the three authors of this paper further undertake the fol-lowing validation steps: (1) Assessing Instruction Clarity and Relevance: They examine whether the instructions are clear, contextually related to the session topic, and align with the ongoing conver-sation. (2) API Sequence Execution: The authors execute the provided API sequences to identify and rectify coding errors. (3) Goal Achievement Check: They verify if the instructions’ intended goals are successfully completed in the label files. In the event that errors are identified during this validation process, the authors promptly report them to the respective workers for revision. The three authors are computer science senior students and researchers.
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2.3 PPTX-Match Evaluation System
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We design the PPTX-Match Evaluation System to evaluate LLMs’ performance on the PPTC bench-mark. Specifically, our PPTX-Match Evaluation System first uses a Python-PPTX Content Reader Module to iterate over all shapes in the prediction PPT file produced with the LLM and the label out-put file. A shape in the PPTX library typically refers to an individual object, such as a text box or table. Then our system extracts attributes like text, style, and position of the shapes using the PPTX library. Next, we check all attributes from the prediction PPT file. For non-position attributes (e.g., text content), we first convert it and the cor-responding attribute in the label PPT file into two strings, and then we use the Exact Match method to examine if the two strings are the same. If they are different or we do not find the corresponding attribute in the label file, then we find an incorrect match. For the position attribute (e.g., location in-formation), we focus on checking if the objects in the prediction PPT file satisfy the correct position relation <A, B, REL>, where A and B are objects that should satisfy the relation REL. In the bench-mark collection process, we ask crowd workers to label the position relation that objects should sat-isfy to finish the instruction. During the evaluation Figure 3: Statistics for PPTC. a) Session turn number distribution. b) Instruction API number distribution (tokens). c) Distribution of instructions involving Chart, Table, Picture, and Position. Instructions involving ’Position’ need the system to conduct position-related operations based on the understanding of spatial information. Note that one instruction may involve multiple different modalities.
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phase, we extract the position attributes of objects A and B and use predefined functions to verify if the objects’ position attributes satisfy the position relation. If there are no incorrect matches for all non-position attributes and no rule violations for all position-related attributes, we consider the LLM has successfully completed the user instruction.
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2.4 Benchmark Statistics Analysis
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To understand the properties of PPTC, we analyze the instructions and APIs in the benchmark. Specif-ically, we explore (i) the number of turns in a ses-sion, (ii) the difficulty of the instruction in terms of the number of APIs required to finish it, and (iii) the number of multi-modality instructions. We report statistics about the PPTC benchmark in Figure 3.
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The number of turns in a session The session turn number distribution (Figure 3 (a)), measured as the number of turns in a session, shows that all sessions in our benchmark have at least two turns and almost all sessions have at least 3 turns (be-tween 3 and 13 turns for the 5th to 95th percentile, respectively). The longest session has 17 turns, which is very challenging as the errors made in previous turns can influence the completion of the current instruction.
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Diffculty varies in APIs number The number of APIs in a sequence falls between 1 and 5 for the 5th to 95th percentile (Figure 3 (b)), respec-tively, shows that our instructions’ difficulty varies from a simple sentence that can be finished by one API to a complex instruction that requires the LLM to generate multiple APIs. The longest API se-quence consists of 29 APIs. Generating long API sequences is very challenging as the LLM needs to understand sub-goals in the complex instruction, select appropriate APIs from the file, and generate APIs in a reliable order.
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Rich multi-modal instructions Our benchmark has hundreds of instructions that involve multi-modalities content (Figure 3 (c)). The "chart" modality has the fewest instructions, with 120, while the "position" modality has the most, with 292 instructions. To finish these instructions, LLMs need to employ related-modal APIs based on the understanding of multi-modal file content.
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3 Algorithms
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In this section, we introduce the algorithms we con-sidered to enhance the LLM’s performance in our benchmark. These algorithms can be categorized into two approaches: planning algorithms that help the LLM in decomposing the user instruction and solving it step by step and selection algorithms that assist the LLM in choosing important environmen-tal information or APIs.
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3.1 Planning Algorithms
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Complex user instructions often require multiple intermediate steps to complete. We mainly consider two planning algorithms:
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Zero-shot-CoT (Kojima et al., 2022) enables LLMs to autonomously generate intermediate reasoning processes for complex instruction by prompting LLMs to "Let’s think step by step".
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Tree of Thoughts (ToT) (Yao et al., 2023) en-ables LLMs to follow tree-like reasoning paths, where each tree node represents a thinking state. It leverages LLMs to generate evaluations or votes on different thoughts. 3.2 Selection Algorithms
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Combining the whole PPT file and the whole API file into the LLM’s input can result in an over-whelming amount of redundant information, such as irrelevant file content and unhelpful APIs. Filter-ing the redundant information would improve the efficiency of the LLM. In this context, we primarily focus on two algorithms for selecting the PPT file content and APIs, respectively:
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Content Selection algorithm Firstly, we extract all shapes of the PPT file by Python-PPTX. Next, we prompt the LLM to select the shapes for com-pleting the user’s instruction. We show the prompt in Figure 8, in which we add three demonstration examples to guide the LLM to do selection. In this algorithm, we replace the whole PPT file with the selected shapes when prompting the LLM to generate the API sequence.
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API Selection algorithm The API selection al-gorithm is based on the embedding similarity to select the most relevant APIs for user instructions. Specifically, we use the text embedding API to get the embeddings of all API descriptions and the cur-rent user instruction. Next, we compute the cosine similarity between the instruction embedding and each API description’s embedding and rank them based on the similarity score. In this algorithm, we replace the whole reference API file with the top
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k APIs when prompting the LLM to generate the API sequence.
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4 Experiments
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4.1 Large Language Models Selected for Evaluation
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Here, we assess different cutting-edge large lan-guage models using our benchmark. These chosen models showcase a wide array of capabilities and are highly regarded in the field. The evaluated large language models include 3 closed-source LLMs and 6 open-source LLMs: • GPT-4 (OpenAI, 2023): The latest LLM in the GPT series. GPT-4 is a cutting-edge, large-scale generative pre-trained transformer model. It offers improved performance and a wider knowledge base compared to its prede-cessors. It showcases human-level proficiency in several scenarios. • ChatGPT : ChatGPT is a conversational AI model crafted for dynamic interactions. It’s learned from extensive instruction data and fine-tuned through reinforcement learning with human feedback (RLHF). This empow-ers it to deliver responses that align with hu-man expectations, maintaining context and coherence in conversations. • Text-Davinci-003 (Brown et al., 2020): GPT-3.5 sits between GPT-3 and GPT-4, enhancing performance via additional instruction tuning. It acts as a link between these models, facil-itating comparison. We’ve chosen the Text-Davinci-003 variant from the GPT-3.5 series for our evaluation. • LLaMa-2-Chat (Touvron et al., 2023): LLaMa 2, an auto-regressive open-source lan-guage model, employs an optimized trans-former design. Chat versions utilize super-vised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to match human preferences for helpfulness and safety. • Baichuan-Chat : It is a transformer model trained on approximately 1.2 trillion tokens. It supports both Chinese and English, with a context window length of 4096. • Baichuan-2-Chat (Yang et al., 2023): It is a large-scale multilingual language model trained from scratch, on 2.6 trillion tokens. The chat version uses Supervised Fine-Tuning (SFT) and Reinforcement Learning from Hu-man Feedback (RLHF) to align with humans. • WizardLM v1.2 (Xu et al., 2023a): WizardLM v1.2 is finetuned from LLaMa 2 using super-vised instruction fine-tuning, where instruc-tions are created by rewriting the initial in-structions step by step. • Vicuna v1.5 (16k) (Chiang et al., 2023): Vi-cuna v1.5 (16k) is finetuned from LLaMa 2 us-ing supervised instruction fine-tuning and lin-ear RoPE scaling. It’s trained on about 125K conversations sourced from ShareGPT.com. • Code-LLaMa-instruct (Chiang et al., 2023): Code-LLaMa is a LLaMa-based LLM de-signed for general code completion and un-derstanding. Its instruction version further supports the chat function with users. Code LLaMa models feature a multitask training ob-jective consisting of both autoregressive and causal infilling prediction (predicting the miss-ing part of a program given a surrounding con-text).
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4.2 Experimental Setup
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In this section, we provide an overview of the ex-perimental setup utilized to assess the performance of LLMs on our PPTC benchmark.
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4.2.1 Turn-Based and Session-Based Evaluations
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We consider two performance evaluation ap-proaches in our benchmark: turn-based and session-based evaluations. For the turn-based evaluation, we measure the LLM’s ability to finish a single turn. Specifically, in this evaluation, we assume that the previous turns have been correctly finished, and we prompt the LLM to generate the API sequence to finish the current turn’s user instruction. The prompt consists of the task instruction for finishing the current user instruction, the API file contain-ing feasible APIs, the parsed PPT file content from the PPT file, and dialogue history consisting of instructions of previous turns with their feasible API sequences (see the left of Figure 4). For the session-based evaluation, we measure the LLM’s ability to finish a session containing multiple turns. For all turns in a session, we prompt the LLM to finish them sequentially. The prompt in this eval-uation has two differences: the API solutions for previous turns in dialogue history are the outputs of the LLM instead of the correct API sequences. (2) The PPT content is parsed from the PPT file obtained by executing the previous outputs of the LLM (see the right of Figure 4). That means the error made by LLMs in previous turns would influ-ence subsequent turns.
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Metrics For turn-based evaluation, we report the turn-based accuracy as the ratio of the number of successfully finished turns to the total number of turns. We also report the average token num-ber of the input of one turn and the average API number for finishing one turn as the cost measure-ment. For session-based evaluation, we report the session-based accuracy as the ratio of the number of successfully finished sessions to the total num-ber of sessions. We also report the average value of the token number of all inputs in one session and the average API number required to complete one session as the cost measurement.
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Inference prompt in PPTC
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> (Task instruction ) You are an AI assistant to help the user to operate PowerPoint and edit the contents. Give you the user instruction:<Current user instruc-tion>, you can complete it based on the following APIs and PPT file content. Current you are at page <Page id>. Please finish the user instruction with the functions you have. Don’t generate instructions be-yond what the user has instructed. Don’t guess what the user may instruct in the next step and generete API for them. Don’t use python loop to call API. You can only call API once in one line. If the user does not specify the page to be modified, you can directly start using the APIs without having to navigate to other pages. You need to generate code which can finish the user instruction. The multiple lines of code should be surrounded by <code> and </code> such as: <code> API(); API(); </code> For example, if the user instruction is "create a slide", then the answer should be: <code> create_slide(); </code> (API file )Now, you have access to alist of PowerPoint APIs with the following functions: <APIs and their descriptions> (e.g.,API(name="set_width", parameters="(width)", description="This API sets the width of the selected object.", parameter _description="It takes one parameter ’width’, the width of an object in centimeters as float.", composition _instruction="You should first choose an object before you can change the width of it.", api_desc="width of picture and shapes") ) (PPT file content ) All the PPT contents are: <Begin of PPT>
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> Turn-based: <Parsed PPT file content of the label PPT file of the previous turns> Session-based: <Parsed PPT file content of the LLM prediction file of the previous turns>
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> <End of PPT> (Dialogue history )¬User¬: Hello! ¬AI¬: Hi there! How can I help you? ¬User¬: <the first instruction> ¬AI¬:
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> Turn-based: <the correct feasible API sequence>, Session-based: <the LLM-generated API sequence>
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> ... ¬User¬: <Current user instruction>. Surrounding your answer with <code> and </code>. ¬AI¬:
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Figure 4: The inference prompt we used in both turn-based and session-based evaluation settings. In the turn-based evaluation, we assess the LLM’s performance for the current turn and assume the LLM has correctly finished previous turns. We then use feasible API se-quences of previous turns as the AI response in the dia-logue history and parse the label file of previous turns as the PPT file content. In the session-based evaluation, we evaluate the completion of the entire session and do not assume the LLM has correctly finished previous turns. We use the LLM’s generated API sequences as the response and parsed the LLM prediction file as the PPT file content. 4.3 Implementation Details
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All experiments were conducted using the respec-tive language models’ API provided by Azure Ope-nAI Service †. Azure OpenAI services offer two API types: completion and chat completion. Com-pletion API generates text from prompts, while chat completion API responds based on conversa-tion history and new input. We use the completion API for Text-Davinci-003 and the chat completion API for ChatGPT and GPT-4. We set a temperature of zero for deterministic output and a max token limit of 2048. The frequency penalty and top p are kept at their default values of zero and 1, respec-tively. We use the text-embedding-ada-002 API as the embedding API in the API selection algorithm and set k as 15. For open-source LLMs, we choose the chat version of LLaMa-2, the v1.2 version of WizardLM, and the chat version of Baichuan as our open-source LLMs. We choose the 13 billion parameters model of the three LLMs. For the zero-shot CoT method, we add the sen-tence ’Let’s think step by step’ after the dialogue history of the prompt. For the ToT method, we follow the official code to run it ‡. We run the four algorithm methods based on the GPT-4 model. If the token number of the input prompt is be-yond the token limit, we cut the PPT file content to reduce the token number of the prompt.
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4.4 Main results
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We report the results of LLMs in both turn-based and session-based evaluations in Table 1 and 2. From the results, we highlight the following key findings.
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(1) Superior Performance of GPT-4: GPT-4 consistently outperforms other closed-source and open-source LLMs in both two tasks. Impressively, GPT-4 achieves 75.1% turn-based accuracy in the creating new PPT file task, demonstrating its strong capability to finish one turn of the user instruction. GPT-4 also has a lower API cost compared to other closed-source LLMs since its precise API usage. GPT-4 incurs the highest token expense when edit-ing PPT templates. That is because its higher token limit than other LLMs allows us to input more PPT template content.
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> † en-us/products/cognitive-services/ openai-service
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> ‡ToT: tree-of-thought-llm
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(2) Code continual pre-training and further instruction finetuning can boost open-source LLMs’ performance. : Based on Table 1, it’s evi-dent that current open-source LLMs struggle to match the performance of closed-source LLMs. For example, LLaMa-2-chat only achieves 16.2% turn-based accuracy in the creating new PPT file task, which is far from the performance achieved by closed-source LLMs. We further find that code continual pretraining (Code-LLaMa) and instruc-tion fine-tuning based on LLaMa-2 (WizardLM and Vicuna) can further improve LLaMa-2 per-formance obviously. For example, Code-LLaMa improves LLaMa-2’s turn-based accuracy in the creating new PPT file task by 20.4 %. This obser-vation suggests that there’s untapped potential in open-source LLMs when it comes to our bench-mark, and this potential can be unlocked further by code pre-training and enhancing instruction follow-ing ability.
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(3) Planning and selection algorithms can im-prove LLMs’ turn-based performance From Ta-ble 2, we observe that the planning algorithms (CoT and ToT) can further improve the turn-based per-formance of GPT-4 by 1 ∼2 percent. However, we surprisingly find that the more complex ToT al-gorithm does not outperform the zero-shot CoT algorithm with a 5 ∼10 times token cost. Content and API selection algorithms can further improve the turn-based performance of GPT-4 by 1 ∼ 5 per-cent. That is because they reduce the task difficulty by filtering irrelevant PPT content/APIs in the input prompt. The API selection algorithm also reduces the average token cost by reducing the number of APIs listed in the prompt. However, for the chal-lenging session-based evaluation, these algorithms can not improve GPT-4’s performance or improve it slightly.
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4.5 Three challenges in our PPTC benchmark
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From the result Table 1 and Figure 5. we highlight the following three key challenges.
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(1) Error accumulation makes LLMs perfor-mance poor in finishing the entire multi-turn session. : The performance of all LLMs in handling sessions consisting of multiple turns is notably poor. Even GPT-4, which performs well in turn-based evaluation, achieves only a 22.7% session-based accuracy for the "creating new PPT file" task and a mere 6.0% session-based accuracy for the "editing PPT template" task. Current planning algorithms Models and Methods Creating new PPT Editing PPT template Turn-based Session-based Turn-based Session-based Accuracy Avg token Avg API Accuracy Avg token Avg API Accuracy Avg token Avg API Accuracy Avg token Avg API TD-003 72.6 2.8k 3.0 12.7 20.8k 23.9 24.4 2.9k 8.1 4.0 13.2k 26.6 ChatGPT 70.6 2.9k 3.2 12.7 20.0k 23.4 26.3 4.1k 7.9 2.0 9.2k 22.9 GPT-4 75.1 2.9k 2.9 22.7 20.8k 22.4 38.1 7.5k 7.8 6.0 24.1k 24.7 LLaMa-2 16.4 2.8k 3.9 3.4 21.6k 24.7 8.7 2.2k 7.2 0.0 9.5k 15.6 Code-LLaMa 36.8 2.8k 3.4 0.0 20.7k 32.1 18.7 3k 7.3 2.0 9.6k 22.6 WizardLM 23.9 1.3k 3.3 4.3 12.5k 22.4 10.0 1.3k 5.7 0.0 4.3k 16.5 Vicuna-v1.5 24.3 1.3k 3.9 2.2 11.0k 33.7 6.8 1.3k 6.7 0.0 4.3k 22.7 Baichuan 15.5 1.3k 9.8 0.0 10.9k 44.7 4.4 1.3k 9.6 0.0 4.3k 24.3 Baichuan-2 16.3 1.3k 9.1 3.6 11.6k 48.9 8.7 1.3k 9.2 0.0 4.2k 22.3
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Table 1: We report the results of LLMs in this table.’ TD-003’ is the Text-Davinci-003 model. We directly use the prompts in Figure 4 to prompt LLMs to generate the API sequence.
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> Models and Methods Creating new PPT file Editing PPT template Turn-based Session-based Turn-based Session-based Accuracy Avg token Avg API Accuracy Avg token Avg API Accuracy Avg token Avg API Accuracy Avg token Avg API GPT-4 75.1 2.9k 2.9 22.7 20.8k 22.4 38.1 7.5k 7.8 6.0 24.1k 24.7 GPT-4+CoT 77.0 2.9k 3.1 23.1 20.8k 22.7 40.6 7.5k 8.0 6.0 24.1k 25.2 GPT-4+ToT 76.5 20.8k 3.0 21.8 146.4k 22.6 40.6 81k 7.6 4.0 256.8k 24.0 GPT-4+Content selection 77.5 3.4k 3.0 21.8 24.5k 22.0 43.1 5.8k 8.0 4.0 18.7k 25.2 GPT-4+API selection 76.4 1.5k 2.9 18.8 10.6k 21.3 38.1 7k 8.0 10.0 22.4k 25.8
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Table 2: We report the results of GPT-4 and algorithms based on the GPT-4 model. ’CoT’ and ’ToT’ are the chain of thought and tree of thought algorithms.
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usually fail to improve session-based accuracy. In some cases, they can even make the performance worse. The session-based evaluation is challeng-ing since errors made in previous turns make the LLM fail to finish the session and also influence the completion of the current turn. Also, we need more advanced planning algorithms to complete the multi-turn session.
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(2) LLMs perform badly in processing long PPT template: Current LLMs’ performance in the editing PPT temples task is pretty poor. For exam-ple, the strongest GPT-4 only achieves 38.1% turn-based accuracy and 6.0% session-based accuracy in this task. Other LLMs’ performance is even poorer. The content selection algorithm can partially solve this challenge by filtering out irrelevant file content, but GPT-4 with it still only achieves 43.1% turn-based accuracy. That means current LLMs (e.g., GPT-4) still struggle to handle complex and lengthy PPT templates. For open-source LLMs, there’s a risk of information loss due to token limitations (typically 2 ∼4K tokens limit), which often require truncating lengthy PPT content. When it comes to session-based performance, the accuracy remains nearly zero. This implies that current LLMs are still far from being ideal PPT agents capable of effectively assisting users in editing PPT templates during a multi-turn dialogue session.
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(3) Multi-modal instructions increase the LLM’s failure rate significantly. To assess LLMs’ task completion performance for instructions in-volving multi-modal operations (Table, Chart, Pic-ture, Position, and text), we calculate the average accuracy of GPT-4 for instructions involving each modality, respectively. This is done by dividing the number of correctly completed instructions within each modality by the total number of instructions involving that modality’s operation. The results are presented in Figure 5 (a). From the figure, we observe that GPT-4 performs exceptionally well in the text modality, achieving an accuracy of 85.6%. Its performance becomes poorer when process-ing structured data (Chart and Table), with 12.4% and 16.2% lower accuracy. Instructions involv-ing picture-related operation pose an even greater challenge for GPT-4, as it achieves a 56.8% turn-based accuracy in this modality. GPT-4 exhibits its weakest performance in instructions involving position-related (spatial) operations, with only 24% accuracy. This underscores GPT-4’s limitations in spatial perception ability.
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5 Analysis
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In this section, we analyze the reasons for GPT-4’s errors. We further analyze the influence of model size and dialogue history.
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5.1 Error Analysis of GPT-4 in our benchmark
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To analyze the error made by GPT-4, in our bench-mark, we gather 50 wrong samples for each of the two tasks in our benchmark in the turn-based eval-uation. We find that these wrong samples fall into four error types and visualize the distribution of these four main error types in Figure 5 (b): (1) Po-Figure 5: We illustrate the analysis results of the creating new PPT file task (task 1) and the editing PPT template task (task 2). In sub-figure (a), we report the average turn-based accuracy for instructions involving chart, table, picture, position, and pure text. We don’t draw the accuracy of task 2 as no chart instruction in this task. In sub-figure (b), we report the ratio of four common errors made by GPT-4. In sub-figure (c), we report the accuracy with the model size. We don’t plot the session-based accuracy of task 2 as it is zero.
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sition errors: These occur when GPT-4 struggles with instructions involving position adjustments. For example, when asked to move the shape to the bottom of the slide, GPT-4 wrongly calls the "set _ top" API. position error is the main error in the creating new PPT file task as this task contains many instructions involving position operation. (2) Calling unavailable APIs: GPT-4 sometimes gener-ates APIs that don’t actually exist in the reference API file, resulting in what we call the "API hallu-cination problem." (3) Misunderstanding PPT file content: GPT-4’s comprehension of the PPT con-tent can be flawed, leading to the generation of incorrect API sequences. For example, when in-structed to make the font size of the current slide’s title consistent with previous slides, GPT-4 set a font size that is different from what was used in previous slides’ titles. In the editing template task, misunderstanding the PPT content becomes the main error since this task needs to understand the complex PPT template. (4) Unfollowing Power-point task rules: Completing Powerpoint tasks de-mands a deep understanding of various task rules. For instance, writing a new slide title requires first deleting the original text and then inserting the new title into the text box. However, GPT-4 may di-rectly insert the new content. For the session-based evaluation, we also collect 50 wrong examples. We find that the main reason for the poor session-based performance is the LLM fails to finish the session once it makes an error in one turn of the session. The reasons for errors made in a single turn are similar to those in the turn-based evaluation. One unique phenomenon in this evaluation is that the LLM would repeat previous errors (e.g., employing infeasible APIs) in subsequent turns.
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5.2 Does bigger LLM work better on PPTC?
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To investigate how the model size impacts the LLM’s performance in our benchmark, we con-duct tests using LLaMa-2-chat LLM with 7, 13, and 70 billion parameters and plot the results in Figure 5 (c). We observe that larger LLM consis-tently achieve higher turn-based accuracy for both the creating new PPT and editing PPT template tasks. For example, in the creating new PPT file task, we find that the turn-based accuracy increases from 13.2 (7B) to 30.1 (70B). However, we do not observe a clear positive correlation between model size and session-based performance. One possible explanation is that although the 70B LLM can cor-rectly finish more intermediate steps, it still falls short of completing the entire session. To improve the session-based performance, a larger LLM may be necessary.
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5.3 Does dialogue history help LLMs to generate the API sequence?
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To investigate the influence of dialogue history in our prompt (see Figure 4), we make an ablation experiment for the dialogue history component of our turn-based evaluation prompt §. In this evalua-tion, the dialogue history contains previous turns along with their feasible API sequences. When we removed the dialogue history from the prompt, we observed a decline in GPT-4’s performance. Specif-ically, GPT-4 drops its performance from 75.1 %
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> §The task instruction, current user instruction, API file, PPT content in the prompt are necessary parts for generating the API sequence. So we don’t conduct ablation studies on them.
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to 73.1 % in the creating new PPT file task and decreases its performance by 6.2 % in the editing template task. This experiment shows the positive effect of the dialogue history, as it helps the LLM to both understand the dialogue background and instruct the LLM to correctly use the APIs, similar to few-shot demonstration examples.
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6 Related Works
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Large Language Models like ChatGPT, GPT-4 (Bubeck et al., 2023; OpenAI, 2023), and Bard have billions of parameters and have been trained on the Internet corpus with trillions of tokens. They can write code (Liu et al., 2023a), prove mathemat-ical theorems (Jiang et al., 2022), pass the profes-sional exam (Zhong et al., 2023; Gilson et al., 2023; Katz et al., 2023), and also perform well on other basic natural language tasks (Kim et al., 2023; Jiao et al., 2023; Zhong et al., 2023; Wang et al., 2023b). That raises the hope of achieving artificial general intelligence (AGI). To further boost LLM’s performance on the spe-cific task, one approach involves prompting en-gineerings, such as the chain of thought prompt-ing (Wei et al., 2022; Shi et al., 2022; Yao et al., 2023), self-consistency (Wang et al., 2022) and the least to most prompting (Zhou et al., 2022). An-other approach aims to use feedback to improve per-formance. The self-refine method (Madaan et al., 2023) refines the output through iterative feedback and refinement Provided by LLM itself. The Re-flexion (Shinn et al., 2023) method generates and stores the reflection based on the sparse reward signal and then uses the reflection to induce bet-ter decisions in subsequent trials. The learning to program method (Guo et al., 2023) learns the task program by inducing the general solutions from the errors (feedback) iteratively and uses the program to guide the inference.
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Task completion benchmarks for measuring large language models . To measure LLM’s task completion performance, Saycan (Brohan et al., 2023) and VirtualHome (Puig et al., 2018) bench-marks ask LLM to generate the correct action se-quence for controlling the robot to finish user in-struction. WebShop (Yao et al., 2022) and Android in the wild (Rawles et al., 2023) ask LLM to navi-gate websites and conduct actions to meet the user requirement. APIBench (Patil et al., 2023) and ToolBench (Xu et al., 2023b; Qin et al., 2023b) in-volve selecting and using APIs to complete the task instruction. Agentbench (Liu et al., 2023b)assesses LLM as autonomous agents in 8 environments and WebArena (Zhou et al., 2023) considers task com-pletion in web-based interactions.
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AI assistant system for complex task com-pletion For more complex tasks that involve us-ing tools and utilizing environmental information, there are many strong AI systems (e.g., TaskMa-trix (Liang et al., 2023)) that help the user finish the complex task. One approach involves connecting massive models and tools with the Large Language Model for task completion. Examples include Visual ChatGPT (Wu et al., 2023) and Hugging-GPT (Shen et al., 2023) which use LLM to deploy task-specific models to finish the user instruction based on the observation of task information (e.g., visual information), Voyager (Wang et al., 2023a) that uses the fixed LLM to continually learn skills (tools) based on the observation of the Minecraft environment. Another approach involves training an end-to-end LLM to finish the user instruction. Examples include Gorilla (Patil et al., 2023) for generating API calls to finish the user query by using the API bench to fine-tune the pre-trained LLaMA (Touvron et al., 2023) model. PaLM-E (Driess et al., 2023) for various robot reasoning tasks by fine-tuning the pretrained PaLM (Chowd-hery et al., 2022) model using features from sensor modalities. Different from the above systems, we focus on the research topic of the AI assistant sys-tem in office software.
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7 Conclusion
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We introduce the PowerPoint Task Completion benchmark to measure LLMs’ ability to complete user instructions within the context of the Power-Point software. It contains hundreds of multi-turn sessions with different topics and thousands of in-structions with varying levels of difficulty. We fur-ther propose the PPTX-evaluation system to access and compare the performance of different LLMs. Results show that GPT-4 is the strongest LLM but still performs poorly in finishing entire sessions. We further analyze the behavior of LLMs and find three main error factors that limit their performance. Our benchmark and findings can help the research community design better AI task completion assis-tants. 8 Limitations
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Our benchmark does not consider instructions that involve subjective evaluation. For example, the user may want to make the slide more beautiful. However, it’s hard to automatically evaluate if the generated file (the model output) is more beautiful. Another limitation is that we do not consider the instructions that need non-API operations. For ex-ample, the user may want to draw a cat on the slide. That instruction needs the AI-assistant system to draw the cat by dragging the mouse and is still in-feasible for LLMs and LLM-based systems. We only consider instructions that can be completed by directly executing the API sequence.
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A The API Reference File
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We list all APIs and their descriptions in Figures 6 and 7. We provide 49 feasible APIs.
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B The Prompt for Content Selection Algorithm
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We put the prompt of content selection algorithm in Figure 8. API reference file
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Slide-related APIs
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API: create slide(): This API creates a new slide. API: move to previous slide(): This API moves to the previous slide. API: move to next slide(): This API moves to the next slide. API: move to slide(slide id): This API moves to the slide with given slide id.It takes one parameter ’slide id’, the ID of the slide to move to as a integer.
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Choose-related APIs
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API: choose title(): This API selects the title on the slide. You should first call choose title() before inserting text to or changing font attributes of the title. API: choose content(): This API select the content on the slide. You should first call choose content() before inserting text to or changing font attributes of the content. API: choose textbox(idx): This API selects the textbox element on the slide. It takes one parameter, the index of textbox as integer. idx is set to 0 by default, meaning the first textbox. You should first call choose textbox() before inserting text to or changing font attributes of the textbox element. API: choose picture(idx): This API selects the picture element on the slide. It takes one parameter, the index of textbox as integer. idx is set to 0 by default, meaning the first textbox. You should first call choose picture() before changing height, width, rotation of the picture element. You should not call choose picture() before inserting picture element. API: choose chart(): This API selects the chart element on the slide. You should first call choose chart() before changing the chart. You should not call choose chart() before inserting chart element. API: choose shape(shape name): This API selects a specific shape by shape name on the slide. It takes one parameter ’shape name’, the name of the shape to select as a string. shape name can be chosen from [’rectangle’,’right arrow’,’rounded rectangle’,’triangle’,’callout’,’cloud’,’star’,’circle’] You should first call choose shape(shape name) before you can do operations on the shape. You should not call choose shape(shape name) before inserting shape element. API: choose table(): This API selects the table element on the slide. You should first call choose table() before changing the table. You should not call choose table() before inserting table element. API: choose table cell(row id, column id): This API selects a specific cell in the table by giving row id and column id. It takes two parameters, the row id and column id of the cell to select as integers (id starts from 0). Remember the first parameter is row id, the second parameter is column id. You should first call choose table cell(row id, column id) before inserting text.
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Basic APIs
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API: set background color(color): This API sets the background color of the slide. It takes one parameter ’color’, the color name to set as a string, such as ’red’, ’purple’. API: set width(width): This API sets the width of the selected object. It takes one parameter ’width’, the width of an object in centimeters as float. You should first choose an object before you can change the width of it. API: set height(height): This API sets the height of the selected object. It takes one parameter ’height’, the height of an object in centimeters as float. You should first choose an object before you can change the height of it API: rotate element(angle): This API rotates the selected element by the specified angle. It takes one parameter ’angle’, the angle to rotate clockwise as integer. You should first choose an object before you can rotate it. API: set fill color(color): This API sets the fill color of the selected object after the object is chosen. It takes one parameter ’color’, the color name to set as a string, such as ’red’, ’purple’. You can set the fill color of content, title or textbox. API: set left(left): This API moves and changes the object’s position. It sets the x position of the selected object’s leftmost point. It takes one parameter, the x position to set. You should first choose an object before you can change the left of it API: set top(top): This API moves and changes the object’s position. It sets the y position of the selected object’s upmost point. It takes one parameter, the y position to set. You should first choose an object before you can change the top of it.
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> Figure 6: The reference API file: part 1.
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API reference file
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Text-related APIs
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API: insert text(text): This API inserts text into a text frame (textbox, title, content, table). API: insert bullet point(text): This API inserts a bullet point into the content. It takes one parameter, the text of the bullet point to insert as a string. API: insert note(text): This API inserts a note onto the slide. It takes one parameter, the note text to insert as a string. API: insert textbox(): This API inserts a textbox onto the slide. When you need to add a caption or text under/above/left to/right to an object, you can call insert textbox(). API: delete text(): This API delete the text part of an object. You should first choose content or title before you can call delete text() API: set font size(font size): This API sets the size of the font It can take one argument ’font size’, the font size to set as an integer. API: set font color(color): This API sets the color of the font. It takes one parameter ’color’, the color name to set as a string, such as ’red’, ’purple’. API: set font bold(): This API sets the font to be bold. API: set font italic(): This API sets the font to be italic. API: set font underline(): This API sets the font to be underlined. API: set font style(font name): This API sets the font style of the selected text. It can take one argument ’font style’, the font name as a string. API: set line space(line space level): This API sets the line spacing of the selected text. It can take one argument ’line space level’, as an integer, default 0. API: text align left(): This API aligns the text to left. API: text align center(): This API aligns the text to center. API: text align right(): This API aligns the text to right.
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Image and shape-related APIs
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API: insert picture(picture name): This API inserts a picture onto the slide. It takes one parameter ’picture name’, the name or description of picture as a string API: insert rectangle(): This API inserts a rectangle or square shape onto the slide. API: insert right arrow(): This API inserts an arrow shape onto the slide. API: insert rounded rectangle(): This API inserts a rounded rectangle shape onto the slide. API: insert triangle(): This API inserts a triangle shape onto the slide. API: insert callout(): This API inserts a callout shape onto the slide. API: insert cloud(): This API inserts a cloud shape onto the slide. API: insert star(): This API inserts a star shape onto the current slide. API: insert circle(): This API inserts a circle or oval shape into the current slide.
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Table-related APIs
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API: insert table(row num, col num): This API inserts a table of row num rows and col num columns onto the current slide. It takes two argument, the row number and the column number of the inserted table as integer. Remember the first parameter is row number and the second parameter is column number. API: insert table row(row data): This API inserts a row (list) of data into the table. It takes one argument, the data to insert as a list of numbers or strings. You should first call choose table() before you can call insert table row(). The parameter ’row data’ should be a list of strings.
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Chart-related APIs
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API: insert line chart(data, series): This API inserts a line chart onto the slide. It takes two argument, ’data’ is a list of numbers and ’series’ is a list of strings. API: insert bar chart(data, series): This API inserts a bar chart onto the slide. It takes two argument, ’data’ is a list of numbers and ’series’ is a list of strings. API: insert pie chart(data, series): This API inserts a pie chart onto the slide. It takes two argument, ’data’ is a list of numbers and ’series’ is a list of strings. API: set chart title(title): This API sets the title of a previously inserted chart. It takes one argument ’title’, the title to be set as a string.
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> Figure 7: The reference API file: part 2.
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Content Selection prompt
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You are an AI assistant for PowerPoint. Your task is to determine what kind of content is necessary to fulfill the user’s instruction. You have an API to extract the content, please call the get _content api with correct parameters to fulfill the user’s instruction. You need to extract the minimum necessary information to fulfill user’s instruction.
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Get _content API: get _ content(need _text: Indicates whether text information is required. The text infor-mation encompasses text in title, content, textbox, table, chart, and shape. This parameter is particularly useful when inserting or modifying text of title, content, textbox, table, chart, and shape, or when information about these objects is essential. need _style: Indicates whether style information is required. Style information includes attributes like font type, font size, color, background color, line space, bold, undeline, italic and other visual aspects of objects like rotation. This is useful when changing the appearance of text or objects or when information about an object’s appearance is essential. need _ position: Indicates whether position information is required. The position details encompass an object’s height, width, and its left and top positions. This is crucial when moving objects or altering an object’s size. need_title: Determines if information related to the title is required. need_content: Determines if information related to the content is required. need_picture: Determines if information related to the picture is required. need_table: Determines if information related to the table is required. need_chart: Determines if information related to the chart is required. need_textbox: Determines if information related to the textbox is required. need _shape: Determines if information related to the shapes (rectangle, right arrow, rounded rectangle, triangle, callout, cloud, star, circle) is required. ) Where the parameters are either 1 (needed) or 0 (not needed). You should only answer with calling get _content() with the right parameters.
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For examples:
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| 256 |
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Instruction: Increase the font size of the content to 20. Explanation: For information, style information (font size) is needed. For objects, content is needed. Answer: get_content(need_text=1,need_style=1,need_position=0, need_title=0,need_content=1,need_picture=0,need_ table=0,need_chart=0,need_textbox=0,need_shape=0)
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|
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... Given the instruction, output the Answer without Explanation:
|
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|
| 261 |
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Instruction: <Current user instruction> Answer:
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| 262 |
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|
| 263 |
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Figure 8: The prompt of the content selection algorithm.
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articles/libreoffice_impress/05dd4c1d-c489-4c85-8389-a7836c4f0567/00_task-source-url/images.json
ADDED
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{
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"extracted_count": 0,
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"downloaded_count": 0,
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"failed_count": 0,
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"downloaded": [],
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"failed": [],
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"pruned": [],
|
| 8 |
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"pruned_count": 0
|
| 9 |
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}
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articles/libreoffice_impress/05dd4c1d-c489-4c85-8389-a7836c4f0567/00_task-source-url/meta.json
ADDED
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{
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| 2 |
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"task_id": "05dd4c1d-c489-4c85-8389-a7836c4f0567",
|
| 3 |
+
"category": "libreoffice_impress",
|
| 4 |
+
"instruction": "Align the text of the first textbox on slide 3 to the right, on slide 4 to the center, and on slide 5 to the left. Ensure that the alignment is applied correctly to each respective slide.",
|
| 5 |
+
"task_source": "https://arxiv.org/pdf/2311.01767.pdf",
|
| 6 |
+
"search_query": "LibreOffice Impress Align the text of the first textbox on slide 3 to the right, on slide 4 to the center, and on slide 5 to the left. Ensure that the alignment is applied correctly to each respective slide. tutorial guide how to",
|
| 7 |
+
"rank": 0,
|
| 8 |
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"title": "Task source URL",
|
| 9 |
+
"url": "https://arxiv.org/pdf/2311.01767.pdf",
|
| 10 |
+
"domain": "arxiv.org",
|
| 11 |
+
"snippet": "Ground-truth source URL from the OSWorld task JSON.",
|
| 12 |
+
"serper_source": "task_source",
|
| 13 |
+
"fetch": {
|
| 14 |
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"ok": true,
|
| 15 |
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"method": "jina",
|
| 16 |
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"status_code": 200,
|
| 17 |
+
"final_url": "https://r.jina.ai/https://arxiv.org/pdf/2311.01767.pdf",
|
| 18 |
+
"error": null,
|
| 19 |
+
"fetched_at_unix": 1778968952
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},
|
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"paths": {
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| 22 |
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"article_markdown": "/Users/mercury/Desktop/Research/NIPS26-MetaAgent-RL/OSWorld-private/tutorial_serper_rag_firecrawl/articles/libreoffice_impress/05dd4c1d-c489-4c85-8389-a7836c4f0567/00_task-source-url/article_markdown.md",
|
| 23 |
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"meta_json": "/Users/mercury/Desktop/Research/NIPS26-MetaAgent-RL/OSWorld-private/tutorial_serper_rag_firecrawl/articles/libreoffice_impress/05dd4c1d-c489-4c85-8389-a7836c4f0567/00_task-source-url/meta.json",
|
| 24 |
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"serper_result_json": "/Users/mercury/Desktop/Research/NIPS26-MetaAgent-RL/OSWorld-private/tutorial_serper_rag_firecrawl/articles/libreoffice_impress/05dd4c1d-c489-4c85-8389-a7836c4f0567/00_task-source-url/serper_result.json",
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"firecrawl_raw_json": null,
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"images_json": "/Users/mercury/Desktop/Research/NIPS26-MetaAgent-RL/OSWorld-private/tutorial_serper_rag_firecrawl/articles/libreoffice_impress/05dd4c1d-c489-4c85-8389-a7836c4f0567/00_task-source-url/images.json"
|
| 27 |
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},
|
| 28 |
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"content": {
|
| 29 |
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"word_count": 11342,
|
| 30 |
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"sha256": "8336e5020516863f688354523927eb669a5356c4cd591df9274f0d9aa2da3db9"
|
| 31 |
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},
|
| 32 |
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"images": {
|
| 33 |
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"extracted_count": 0,
|
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"downloaded_count": 0,
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"failed_count": 0
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| 36 |
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}
|
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}
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articles/libreoffice_impress/05dd4c1d-c489-4c85-8389-a7836c4f0567/00_task-source-url/serper_result.json
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{
|
| 2 |
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"title": "Task source URL",
|
| 3 |
+
"link": "https://arxiv.org/pdf/2311.01767.pdf",
|
| 4 |
+
"snippet": "Ground-truth source URL from the OSWorld task JSON.",
|
| 5 |
+
"source": "task_source"
|
| 6 |
+
}
|