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CKA CKA Unlearned Relearned(c) Simple Task ( LR = 3 Γ 10β5, N= 100 ) 0 510 15 20 25 30 Layer index02Linear CKA CKA Unlearned Relearned (d) Simple Task ( LR = 3 Γ 10β5, N= 6) 0 510 15 20 25 30 Layer index02Linear CKA CKA Unlearned Relearned(e) Simple Task ( LR = 3 Γ 10β5, N= 50 ) 0 510 15 20 25 30 Layer index02Linear CK... | https://arxiv.org/abs/2505.16831v1 |
5 10 15 20 25 Layer index02Linear CKA CKA Unlearned Relearned(i) Complex Task ( LR = 3 Γ 10β5, N= 6) Figure 17: CKA Across Layers. Each row shows results under different unlearning methods: GA (a-c) NPO (dβf), Rlable (gβj). All plots are for the complex task on Qwen2.5-7B, using three learning rates{3Γ10β6,5Γ10β6,3Γ10β... | https://arxiv.org/abs/2505.16831v1 |
rates{3Γ10β6,5Γ10β6,3Γ10β5}and fixed N= 100 . 27 1010 107 104 101 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 31 Original Unlearned Relearned(a) LR= 3Γ10β6, Layer 31 109 105 101 103 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 31 Original Unlearned Relearned (b) LR= 5Γ10β6, Layer 31 1017 ... | https://arxiv.org/abs/2505.16831v1 |
102106 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 22 Original Unlearned Relearned (g) LR= 3Γ10β6, Layer 22 1010 106 102 102106 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 22 Original Unlearned Relearned (h) LR= 5Γ10β6, Layer 22 1015 1012 109 106 103 Fisher Diagonal Value (log scale)012F... | https://arxiv.org/abs/2505.16831v1 |
Original Unlearned Relearned (m) LR= 3Γ10β6, Layer 4 1016 1011 106 101 Fisher Diagonal Value (log scale)0.02.55.0Frequency1e8FIM @ Layer 4 Original Unlearned Relearned (n) LR= 5Γ10β6, Layer 4 1015 109 103 103 Fisher Diagonal Value (log scale)0.02.55.0Frequency1e8FIM @ Layer 4 Original Unlearned Relearned (o) LR= 3Γ10β5... | https://arxiv.org/abs/2505.16831v1 |
Yi-6B, using three learning rates {3Γ10β6,5Γ10β6,3Γ10β5}and fixed N= 100 . 31 109 105 101 103 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 31 Original Unlearned Relearned(a) LR= 3Γ10β6, Layer 31 1012 108 104 100 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 31 Original Unlearned Relearned (... | https://arxiv.org/abs/2505.16831v1 |
Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 13 Original Unlearned Relearned (g)N= 6, Layer 13 107 10010710141021 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 13 Original Unlearned Relearned (h)N= 50 , Layer 13 1018 1013 108 103 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 1... | https://arxiv.org/abs/2505.16831v1 |
Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 1 Original Unlearned Relearned (q)N= 50 , Layer 1 1020 1013 106 101 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 1 Original Unlearned Relearned (r)N= 100 , Layer 1 Figure 26: FIM for GA+GD Across Layers. Simple task on Yi-6B with fixed learning ... | https://arxiv.org/abs/2505.16831v1 |
Original Unlearned Relearned (d)N= 6, Layer 28 1011 108 105 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 28 Original Unlearned Relearned (e)N= 50 , Layer 28 1011 108 105 102 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 28 Original Unlearned Relearned (f)N= 100 , Layer 28 1011 108 105 Fishe... | https://arxiv.org/abs/2505.16831v1 |
13 Original Unlearned Relearned (k)N= 50 , Layer 13 1010 107 104 101 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 13 Original Unlearned Relearned (l)N= 100 , Layer 13 1016 1011 106 101 Fisher Diagonal Value (log scale)0.02.55.0Frequency1e8FIM @ Layer 4 Original Unlearned Relearned (m)N= 6, Layer 4 1016 1... | https://arxiv.org/abs/2505.16831v1 |
@ Layer 1 Original Unlearned Relearned (r)N= 100 , Layer 1 Figure 30: FIM for Rlable Across Layers. Simple task on Yi-6B with fixed learning rate LR = 3Γ10β5and varying unlearning requests Nβ {6,50,100}. 38 1010 10110121023 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 28 Original Unlearned Relearned(a) L... | https://arxiv.org/abs/2505.16831v1 |
13 Original Unlearned Relearned (i) LR= 3Γ10β5, Layer 12 1017 1013 109 105 101 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 4 Original Unlearned Relearned (j) LR= 3Γ10β6, Layer 4 1017 1012 107 102 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 4 Original Unlearned Relearned (k) LR= 5Γ10β6, L... | https://arxiv.org/abs/2505.16831v1 |
scale)012Frequency1e8FIM @ Layer 31 Original Unlearned Relearned(b) Simple (Relearned by retain set), Layer 31 1015 109 103 103 Fisher Diagonal Value (log scale)012Frequency1e8FIM @ Layer 31 Original Unlearned Relearned(c) Simple (Relearned by unrelated data), Layer 31 1015 109 103 103 Fisher Diagonal Value (log scale)... | https://arxiv.org/abs/2505.16831v1 |
Fisher Diagonal Value (log scale)0.02.55.0Frequency1e8FIM @ Layer 4 Original Unlearned Relearned (a) Simple (Relearned by forget set), Layer 4 1015 108 101 106 Fisher Diagonal Value (log scale)0.02.55.0Frequency1e8FIM @ Layer 4 Original Unlearned Relearned(b) Simple (Relearned by retain set), Layer 4 1015 108 101 106 F... | https://arxiv.org/abs/2505.16831v1 |
From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization Haonian Ji1*, Shi Qiu1*, Siyang Xin1*, Siwei Han1* Zhaorun Chen2, Dake Zhang3, Hongyi Wang3, Huaxiu Yao1 1UNC-Chapel Hill,2University of Chicago,3Rutgers University Abstract While foundation models (FMs... | https://arxiv.org/abs/2505.16832v2 |
obstacles first requires 1arXiv:2505.16832v2 [cs.AI] 27 May 2025 a clear picture of how current FMs perform, so that future models can be purpose-built to close the gaps. Consequently, a comprehensive evalua- tion platform is critical for systematically assessing FMs on visual pedagogical reasoning. To bridge this gap,... | https://arxiv.org/abs/2505.16832v2 |
five key dimen- sions: (1) the logical sequencing of visual elements, (2) the structural richness of the visuals, (3) seman- tic alignment with the underlying subject matter, (4) the clarity and guidance provided for problem- solving, and (5) the level of interactivity and en- gagement. In the following subsections, we... | https://arxiv.org/abs/2505.16832v2 |
A.1 to guide the scoring process. Rubrics and Evaluation Procedure. To ensure a systematic and replicable evaluation pipeline, we manually construct detailed scoring rubrics for each of the five evaluation dimensions, with clear criteria defined for every level on a 0β5 scale. The visual outputs generated by each model... | https://arxiv.org/abs/2505.16832v2 |
structured formulation provides a pedagogically grounded foundation that guides the downstream agents in generating coherent, targeted, and educa- tionally effective visual explanations. 3.2 Collaborative Solution Generation In this stage, EduVisBench executes the instruc- tional task constructed by sequentially activa... | https://arxiv.org/abs/2505.16832v2 |
2024), MiniCPM- V2.6 (Yao et al., 2024), Mistral-Small-3.1-24B- Instruct-2503 (Mistral AI, 2025), Phi-3.5-Vision- Instruct (Abdin et al., 2024), Phi-4-Multimodal- Instruct (Abouelenin et al., 2025), Qwen2.5-VL- 72B (Team, 2025), GPT-4o (Hurst et al., 2024), Claude 3.7 Sonnet (Anthropic, 2025), and Gemini 2.0 Flash (Mal... | https://arxiv.org/abs/2505.16832v2 |
the latterβs significantly better performance with Webpages (54.6%) over SVG (42.0%). GPT-4o also showed a preference for Webpage generation (38.1%) over SVG (26.3%), suggesting that prompting advanced LVLMs for structured interactive webpages can yield more effective visual explanations. Nevertheless, even these top-t... | https://arxiv.org/abs/2505.16832v2 |
EduVisAgent(Ours) The image fails to illustrate the chemical process, lacks a clear step-by-step instructional approach, and offers no intuitive visual representation. β¦β¦ Figure 6: Baseline models versus our EduVisAgent. These examples clearly demonstrate the often poor output qual- ity of baseline models, contrasting ... | https://arxiv.org/abs/2505.16832v2 |
text-based interac- tions (Wu et al., 2023; Xu et al., 2024; Cui et al., 2024), overlooking the critical role of visualization in fostering conceptual understanding and improv- ing learning outcomes (Presmeg, 2006). Despite its pedagogical importance, the capacity of FMs and agents to generate logical, explanatory visu... | https://arxiv.org/abs/2505.16832v2 |
Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, and 1 others. 2024. Mle-bench: Evaluating machine learn- ing agents on machine learning engineering. arXiv preprint arXiv:2410.07095. Jiawen Chen, Jianghao Zhang, Huaxiu Yao, and Yun Li. 2025a. Celltypeagent: Trustworthy cell type an- n... | https://arxiv.org/abs/2505.16832v2 |
arXiv preprint arXiv:2310.06770. 9 Hyoungwook Jin, Minju Yoo, Jeongeon Park, Yokyung Lee, Xu Wang, and Juho Kim. 2025. Teachtune: Reviewing pedagogical agents against diverse stu- dent profiles with simulated students. In Proceedings ofthe2025 CHI Conference onHuman Factors in Computing Systems, pages 1β28. Max Ku, Tho... | https://arxiv.org/abs/2505.16832v2 |
2025. Paperbench: Evaluating aiβs ability to replicate ai research. arXiv preprint arXiv:2504.01848. Qwen Team. 2025. Qwen2.5-vl. Pere-Pau VΓ‘zquez. 2024. Are llms ready for visual- ization? In 2024 IEEE 17th Pacific Visualization Conference (PacificVis), pages 343β352. IEEE. Vercel. 2025. v0: Ai chat interface for web ... | https://arxiv.org/abs/2505.16832v2 |
Wei Liu, Qian Tan, Jingdan Chen, Hang Yan, Yuliang Yan, Jiatong Li, Weiran Huang, Xi- angyu Yue, Dongzhan Zhou, Shufei Zhang, Mao Su, Hansen Zhong, Yuqiang Li, and Wanli Ouyang. 2024a. Chemllm: A chemical large language model. Preprint, arXiv:2402.06852. Kechi Zhang, Jia Li, Ge Li, Xianjie Shi, and Zhi Jin. 2024b. Code... | https://arxiv.org/abs/2505.16832v2 |
no visual aids to complex integrated dashboards that deeply connect data and model structures. Through five levels, the framework guides designers to improve clarity, coherence, and contextual richness of visual illustrations, enhancing studentsβ analytic and comparative abilities. 12 Level Description Level 1The image... | https://arxiv.org/abs/2505.16832v2 |
two title-style hints (e.g., βIdentify problem type,β βCheck resultβ), but the flowchart is overly simplistic and hints lack hierarchical detail. Level 3The image displays a step-by-step flowchart template with key thinking nodes and self-check checkpoints, leaving annotation space for students to visually record their... | https://arxiv.org/abs/2505.16832v2 |
standard β’ Evaluation should be based solely on the specified aspect: {category} . The definition of {category} is:{description} . β’Do not include any explanation, justification, or additional commentary. Refusing to provide a score is not allowed. Evaluation Output Format {{RATING: {"1":score, "2":score, "3":score, "4... | https://arxiv.org/abs/2505.16832v2 |
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis Shuang Sun1*, Huatong Song1*, Yuhao Wang1, Ruiyang Ren1, Jinhao Jiang1, Junjie Zhang1, Fei Bai1, Jia Deng1, Wayne Xin Zhao1β , Zheng Liu2, Lei Fang3, Zhongyuan Wang2, Ji-Rong Wen1 1Gaoling School of Artificial Intelligence, Renmi... | https://arxiv.org/abs/2505.16834v2 |
oriented purposes on the Web, which are essential for deep search training. On the output side, tradi- tional answer annotations omit the critical reason- ing traces (search operations, evidence synthesis, and efficient decision paths) required for teaching search-integrated reasoning strategies. In this paper, we prop... | https://arxiv.org/abs/2505.16834v2 |
efficiency, providing a simple yet powerful approach to enhancing deep search ca- pabilities. Furthermore, our framework is highly extensible that can be combined with other types of training data, the framework is also applicable to RL-based training.Our main contributions are as follows: β’We propose a real web-based ... | https://arxiv.org/abs/2505.16834v2 |
open, dynamic, and com- plex web environments where the information is not only diverse in format and varied in quality but is also frequently accompanied by redundancy and noise. In light of this, our data synthesis approach does not rely on curated document collections but is instead grounded in the real, open web en... | https://arxiv.org/abs/2505.16834v2 |
thereby reduc- ing domain-specific biases and enhancing general- ization capabilities. Keyword Diversity focuses on the distributional diversity of core semantic constituents. we ensure non-redundant exposure to low-frequency concep- tual entities, multi-order relational dependencies, and contextually ambiguous referen... | https://arxiv.org/abs/2505.16834v2 |
model can learn to seamlessly integrate search into its inference pro- cess and adopt more efficient reasoning strategies. Question Difficulty . Filter out questions with con- sistently high accuracy across multiple reasoning attempts and prioritize those with lower accuracy. Accuracy obtained from multiple samples can... | https://arxiv.org/abs/2505.16834v2 |
using the following model backbones with an online search engine, including Qwen-2.5- 7B-Instruct, Qwen-2.5-32B-Instruct, Deepseek- Distilled-Qwen-2.5-32B, and QwQ-32B. Implementation Details. Our experimental setup consists of four main components: query Models Methods2Wikiβ MuSiQueβ Bamboogleβ‘Framesβ‘GAIAβ‘ F1 LasJ F1 La... | https://arxiv.org/abs/2505.16834v2 |
70.4 35.6 35.8 w/o KD 73.2 76.0 32.9 31.1 w/o CKU 71.7 74.4 32.1 29.1 Environment w/o Online 74.0 74.4 30.4 28.2 Response Curationw/o FS 72.8 75.2 38.0 36.9 w/o RPC 71.7 74.4 31.6 30.1 w/o QD 67.1 70.4 32.9 32.0 w/o SE 72.6 73.6 37.7 35.0 Table 2: Results of variants of SimpleDeepSearcher on Bamboogle and GAIA. Bamboog... | https://arxiv.org/abs/2505.16834v2 |
and online reinforcement learning, respec- tively. As shown in Table 3, the model trained with DPO achieves further improvements over the SFT baseline, demonstrating the effectiveness of offline preference optimization. In contrast, the model trained with REINFORCE++ produces sig- nificantly shorter responses (see Appe... | https://arxiv.org/abs/2505.16834v2 |
of overall performance using different summa- rization models. As shown in Table 6, QwQ-32B demonstrates the strongest summarization capa- bility and is therefore selected as the summariza- tion model for all reasoning models. Furthermore, using fine-tuned models for summarization leads to performance degradation on do... | https://arxiv.org/abs/2505.16834v2 |
Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. Self-rag: Learning to retrieve, generate, and critique through self-reflection. InThe Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net. Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu, Zach Bran... | https://arxiv.org/abs/2505.16834v2 |
Qi Liu. 2025. Hoh: A dynamic benchmark for evaluating the impact of out- dated information on retrieval-augmented generation. arXiv preprint arXiv:2503.04800 . Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. 2022. Measuring and narrowing the compositionality gap in language models. arXi... | https://arxiv.org/abs/2505.16834v2 |
via reinforce- ment learning in real-world environments. arXiv preprint arXiv:2504.03160 . A Related Work Retrieval-Augmented LLMs. To improve the fac- tual precision of LLM-generated texts (Zhao et al., 2024), researchers enhance LLMs with retrieval- augmented generation (RAG) (Guu et al., 2020). Various approaches ha... | https://arxiv.org/abs/2505.16834v2 |
diversity in the RL training data. The reward function employed in REIN- FORCE++ consists of two components: an answer reward and a format penalty. The answer reward is calculated as the F1 score between the predicted answer and the reference answer, providing a di- rect measure of response accuracy. In addition, a dis... | https://arxiv.org/abs/2505.16834v2 |
Answer. Respond with True if the prediction is correct and False otherwise. Golden Answer may have multiple options, and matching any one of them is considered correct. \n Question: {question} Golden Answer: {reference} Predicted Answer: {prediction} Figure 5: Instruction for LLM as a judge. Instruction for Reasoning M... | https://arxiv.org/abs/2505.16834v2 |
arXiv:2505.16838v1 [cs.CL] 22 May 2025R1-Compress: Long Chain-of-Thought Compression via Chunk Compression and Search Yibo Wang1Li Shen2βHuanjin Yao1Tiansheng Huang Rui Liu3Naiqiang Tan3Jiaxing Huang4Kai Zhang1Dacheng Tao4 1Tsinghua University2Sun Yat-sen University 3Didichuxing Co. Ltd4Nanyang Technological University... | https://arxiv.org/abs/2505.16838v1 |
a gap from the natural language patterns typically used by LLMs. Based on the above finding, it seems that effectively compressing Long-CoT cannot be achieved solely through instance-level or token-level methods. Therefore, we propose a chunk-level compres- sion approach, which better preserves chunk-level local inform... | https://arxiv.org/abs/2505.16838v1 |
and Graph of Thought (GoT) [ 1] expand upon the CoT architecture by investigating various reasoning trajectories or integrating structures based on networks. Besides, Chain-of-thought reasoning also enable human to comprehend the modelβs decision-making pathway, thereby rendering the reasoning process both transparent ... | https://arxiv.org/abs/2505.16838v1 |
the reasoning level; LightThinker[ 37] dynamically compress intermediate thoughts during reasoning and Sleep-time Compute[ 13] allows models to "think" offline about contexts before queries are presented. Additionally, methods like COCONUT[ 7] and CCOT[ 5] enable reasoning in the latent space. Besides these, some other... | https://arxiv.org/abs/2505.16838v1 |
- 0.048 \right) \lg \left(x^3 + 2x + 1 \right) = 0 \), we need toβ¦The equation \(x^2 + 2 = 0 \) has no real solutions because \(x^2 = -2\) is not possible for real numbers. Therefore, the only solution from Case 2 is \(x = 0 \).**Verification **We need toβ¦For \(x = -1\): First, check the logarithm term:β¦ Check the loga... | https://arxiv.org/abs/2505.16838v1 |
reason- ing. Since the presence of reflection signif- icantly affects the reasoning ability of fine- tuned models, a more fine-grained com- pression strategy is needed to effectively retain such local information. 3.3 Revisiting Token-level Compression Token-level methods are inherently capable of preserving local info... | https://arxiv.org/abs/2505.16838v1 |
methods often produce incoherent CoT, which can negatively impact the training process and lead to models that generate incoherent outputs. In contrast, instance- level methods, such as C3oT, compress global information through prompting LLMs, resulting in more coherent outputs. 3.4 Derived Insight Based on the above a... | https://arxiv.org/abs/2505.16838v1 |
employ a greedy search approach: Length-based Filtering: For each position i, we first filter the set of Mcandidates {Λcj i}M j=1. We discard the Ξ±Β·Mlongest candidates, keeping the (1βΞ±)Mshortest ones, where Ξ±β[0,1)is a predetermined filtering ratio. Let the filtered set of candidates for position ibeΛCiβ {Λcj i}M j=1.... | https://arxiv.org/abs/2505.16838v1 |
compression, we utilize LLaMA3.1-70B-Instruct with a sampling temperature of 0.75 and generate 8 candidate chunks. The chunk search method is performed by the DeepSeek-R1-Distill-Qwen-14B model, More details are in Appendix A.1. 5.2 Main Results For the Qwen2.5-14B-Instruct model in Table 2, we observe that R1-Compress... | https://arxiv.org/abs/2505.16838v1 |
as input and the original uncompressed CoT as reference, we evaluate the average token-level loss for TokenSkip, R1-Compress random , R1-Compress. Table 4 reports the average token-level loss for each method. The results indicate that both of our methods achieve lower token-level loss compared to TokenSkip, indicating ... | https://arxiv.org/abs/2505.16838v1 |
combining inner-chunk compression with an inter-chunk search mech- anism. Unlike existing approaches that either compromise critical reasoning behaviorsβsuch as reflectionβor lead to incoherent outputs, R1-Compress effectively reduces token length while main- taining high reasoning quality. Experimental results across ... | https://arxiv.org/abs/2505.16838v1 |
Mei, J. Fang, Z. Guo, L. Song, and C.-L. Liu. From system 1 to system 2: A survey of reasoning large language models, 2025. [13] K. Lin, C. Snell, Y . Wang, C. Packer, S. Wooders, I. Stoica, and J. E. Gonzalez. Sleep-time compute: Beyond inference scaling at test-time, 2025. [14] H. Luo, H. He, Y . Wang, J. Yang, R. Li... | https://arxiv.org/abs/2505.16838v1 |
large language models, 2025. [28] R. Wang, H. Wang, B. Xue, J. Pang, S. Liu, Y . Chen, J. Qiu, D. F. Wong, H. Ji, and K.-F. Wong. Harnessing the reasoning economy: A survey of efficient reasoning for large language models, 2025. [29] Y . Wang, S. Wu, Y . Zhang, S. Yan, Z. Liu, J. Luo, and H. Fei. Multimodal chain-of-th... | https://arxiv.org/abs/2505.16838v1 |
and long models, respectively, to create different variants of the Short-Long-Short model. These merged models are then used to sample and construct the MixChain of Short-Long-Short CoT dataset. TokenSkip : This baseline directly applies token-level compression to Long-CoT to generate shortened CoT. We follow its setti... | https://arxiv.org/abs/2505.16838v1 |
practical significance of compressing Long-CoT. Additionally, we observe that on AIME24, the 32B model exhibits a noticeable reduction in valid token length, while the overall token length remains nearly unchanged. This is because AIME24 is a highly challenging task, and when the model produces incorrect answers, it te... | https://arxiv.org/abs/2505.16838v1 |
second set minus the number in both sets. In formula terms:\n\nNumber of coffee drinkers + Number of tea drinkers - Number of both = Total in union\n\nPlugging in the numbers I have:\n\n29 (coffee) + 28 (tea) - Both = 34\n\nSo, 29 + 28 = 57. 1173 characters R1-Compress: (Partial) I need to find out how many teachers dr... | https://arxiv.org/abs/2505.16838v1 |
something like the percentage of x equals the percentage of y but in different terms. Wait, no, in this case, itβs straightforward: two is both 10% of x and 20% of y. So, x and y are different variables, each solved independently. Then subtract. Yeah, thatβs right.\n\nAlternatively, maybe someone might get confused and... | https://arxiv.org/abs/2505.16838v1 |
Highlights β’ Implementation of detailed guidelines and a structured process to en - sure high -quality annotations, focused on targeting language, from the crowd, experts, and ChatGPT to detect and analyze inappropriately targeting language in a comprehensive data set including conversations from banned subreddits β’ In... | https://arxiv.org/abs/2505.16847v1 |
in size, it provides a foundational resource for future research and model evaluation. The study emphasizes advancing knowledge in the field and supporting the develop - ment of improved content moderation systems, rather than serving as a direct tuning tool. Our analysis includes annotations from crowd sources, expert... | https://arxiv.org/abs/2505.16847v1 |
groups, such as race, religion, gender, migrants, and LGBT, their applicability to broader online hate speech contexts is limited. The data set from [5] includes categorized tweets gathered from various sources such as Hatebase, Twitter hashtags, and public hate speech data sets. Human anno - tators classified these tw... | https://arxiv.org/abs/2505.16847v1 |
and selected by [9]. These subreddits were accessed via the Pushshift API and BigQuery. The data set includes 67,677 submissions and 1,168,546 com - ments, totaling 4,017,460 tokens. To ensure meaningful analysis, a structured ap - proach inspired by [8] was employed to reconstruct conversation threads without overlap,... | https://arxiv.org/abs/2505.16847v1 |
individual, political affiliation, and other target category (none of the aforementioned). They marked all relevant words in the comment or title pertaining to these categories, primarily focusing on nouns. Additional examples were given to assist annotators, as shown in Example 3.5, Example 3.6, and Example 3.7, where... | https://arxiv.org/abs/2505.16847v1 |
formats were analyzed and categorized into predefined target categories based on the input from 3.5.1 3.5.3. Target Tokens Annotation We created a prompt directing the model to focus on tokens related to identified target categories. These tokens, extracted from targeting comments or titles provided to ChatGPT along wi... | https://arxiv.org/abs/2505.16847v1 |
on averaged scores from varying annotator pairs. Therefore, this variability is due to the fact that the majority vote method for crowd annotations minimizes individual differ - ences, enhancing apparent agreement with expert annotations, whereas direct comparison among crowd annotators reveals more inherent inconsiste... | https://arxiv.org/abs/2505.16847v1 |
scores given agreement on being targeting Category ChatGPT vs. Adj.Expert (Comment - level) AdjExpert vs. AdjCrowd (Comment - level) ChatGPT vs. AdjExpert (Subthread - level) AdjExpert vs. AdjCrowd (Subthread - level) Inside 0.09 0.55 0.37 0.70 Outside 0.08 0.65 0 0.46 Sexual Orientation 0.66 0.74 0.87 0.75 Gender 0.06... | https://arxiv.org/abs/2505.16847v1 |
significantly depending on the preva - lence of targeting labels and the number of instances being evaluated. At the comment level, with more data points, the absolute number of agreements and disagreements is higher, affecting the expected chance agreement. Additionally, at the subthread level, the aggregation of comm... | https://arxiv.org/abs/2505.16847v1 |
9 20 22.4 6 Outside 59.67 61.8 179 28.33 28.2 38 Sexual Orientation 32.67 24 33 11.33 14.6 12 Gender 24.33 19.2 25 14.67 11.8 9 Disability 20.67 12.2 1 10.67 6.6 1 Age 4.33 5 8 3.33 3.8 7 Race 16 19.8 32 6 8.4 10 Religion 1.67 4.2 3 1.67 3.2 3 Famous Individual 9.33 10.4 1 5.33 8 1 Political Affiliation 13.67 22 19 8.3... | https://arxiv.org/abs/2505.16847v1 |
target. This highlights the importance of contextual information for precise annotation and interpretation. 6. Different interpretation of sarcasm Example 4.6. 20 Context: Title: SO TRUE!! [anon py8O2]: youβre a c**t [anon aWz24]: Youβre 14 [anon ozqK8]: not only a c**t but a dullard too? is their no beginning to your ... | https://arxiv.org/abs/2505.16847v1 |
as subtler discriminatory language and microaggressions. This study contributes a benchmark data set for analyzing targeting language and evaluating automated detection systems, facilitating research into harmful online communication and supporting the development of improved content moderation strategies. We observed ... | https://arxiv.org/abs/2505.16847v1 |
Appendix B. Ethical Considerations This study adhered to ethical guidelines, ensuring the privacy and anonymity of Reddit users and annotators. The annotation task contained content warnings, 23 and participants were informed about the nature of the task before engaging in the annotation process. Appendix C. Annotation... | https://arxiv.org/abs/2505.16847v1 |
First, you should read the following columns per row: βtitle textβ, βcommentβ, βcontextβ, βuser idβ, and βpost textβ. βcontextβ refers to the previous comments. You should read each βtitle textβ, βpost textβ, and βcontextβ. Then, you should read βcommentβ. If βcommentβ is not β-β, you should produce your response based... | https://arxiv.org/abs/2505.16847v1 |
Prompt for ChatGPT annotation of target tokens (Part 2) Text: Why are you just sitting there like a mentally paralyzed person? Target categories: Disability Target tokens: mentally, paralyzed Text: He still acts like a child. Target categories: Age Target tokens: child Text: I donβt give a f**k if you are Asian. Target... | https://arxiv.org/abs/2505.16847v1 |
arXiv:2505.16849v2 [cs.IR] 28 May 2025Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks Martin BΓΆckling martin.boeckling@uni-mannheim.de University of Mannheim Mannheim, GermanyHeiko Paulheim heiko.paulheim@uni-mannheim.de University of Mannheim Mannheim, GermanyAndr... | https://arxiv.org/abs/2505.16849v2 |
multi-step reasoning [6, 21, 23, 30, 37]. Consequently, several works leverage knowledge graphs (KGs) β structured knowledge bases representing real-world information as networks of entities and relations [ 15] β as external information sources to overcome standard RAG limitations [ 37]. Given a query, KG-based RAG sys... | https://arxiv.org/abs/2505.16849v2 |
only a single LLM call per query; (3)enables zero-shot RAG with any off-the-shelf LLM . We show that Walk&Retrieve consistently generates accurate responses, while minimizing hallucinations. Our findings render walk-based corpus generation as a promising approach for scalable KG-based RAG, and establish Walk&Retrieve a... | https://arxiv.org/abs/2505.16849v2 |
we compute each nodeβs global representa- tion from the concatenation of its respective walks. We store the Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks IR-RAG 2025, July 17, 2025, Padua, Italy System : Please provide me from an extracted triple set of a Knowled... | https://arxiv.org/abs/2505.16849v2 |
retrieves subgraphs us- ing a MLP and parallel-triple scoring; the LLM then reasons over the linearized triples of the subgraph to generate a response. Re- trieveRewriteAnswer [53] uses constrained path search and relation 2Note that the preprocessing stepβs computational overhead is a one-time cost, as subsequent grap... | https://arxiv.org/abs/2505.16849v2 |
β RetrieveRewriteAnswer 47.49 34.01 22.92 32.12 22.37 β β β β β Walk&Retrieve-RW 55.60 41.11 15.31 37.13 22.12 19.31 19.40 19.64 51.94 22.15 Walk&Retrieve-BFS 67.99 57.08 12.74 28.27 21.31 21.31 21.53 23.01 53.40 23.34 20 30 40 50 60 70 80 Missing (%)10 0102030405060Truthfulness (%)Walk&Retrieve-RW 1-hop Walk&Retrieve-... | https://arxiv.org/abs/2505.16849v2 |
and fewer unique paths, thus cap- turing less relevant information from the KG.8While they may be more efficient on large-scale KGs, as they do not compute full neighborhoods, this efficiency comes at the cost of increased noise.9 Robustness to Backbone LLMs. Lastly, we evaluate model ro- bustness using different LLMs ... | https://arxiv.org/abs/2505.16849v2 |
(2023). doi:10.48550/arXiv.2310. 05845 [6]Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024. Benchmarking large language models in retrieval-augmented generation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 17754β17762. doi:10.1609/aaai.v38i16. 29728 [7]Marina Danilevsky, Kun Qian, ... | https://arxiv.org/abs/2505.16849v2 |
Generation. Comput. Surveys 55, 12 (2023), 1β38. doi:10.1145/ 3571730 [20] Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, De- vendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al .2023. Mistral 7B. arXiv preprint arXiv:2310.06825 (20... | https://arxiv.org/abs/2505.16849v2 |
35 (2022), 27730β27744. [36] Arjun Panickssery, Samuel R Bowman, and Shi Feng. 2024. Llm evaluators recognize and favor their own generations. arXiv preprint arXiv:2404.13076 (2024). doi:10.48550/arXiv.2404.13076 IR-RAG 2025, July 17, 2025, Padua, Italy BΓΆckling et al. [37] Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, ... | https://arxiv.org/abs/2505.16849v2 |
Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das. 2024. A comprehensive survey of hallucination mitigation techniques in large language models. arXiv preprint arXiv:2401.01313 (2024). doi:10.48550/arXiv.2401.01313 [50] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yas- mine B... | https://arxiv.org/abs/2505.16849v2 |
arXiv:2505.16850v1 [cs.LG] 22 May 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoning Tajamul Ashraf1, Mohammed Mohsen Peerzada5, Moloud Abdar2, Yutong Xie1, Yuyin Zhou3, Xiaofeng Liu4, Iqra Altaf Gillaniβ5, Janibul Bashirβ5 βEqual senior authorship. 1Department of Computer Vision, MBZUAI... | https://arxiv.org/abs/2505.16850v1 |
[arXivβ24] [113], [OJCOMSβ24] [169] β Ours β β β β β β β’Adaptation . In federated learning, data is often generated across diverse sources, naturally resulting in non-independent and non-identically distributed (Non-IID) characteristics [ 134,133,79,173,197, 19,221]. These discrepancies introduce two primary types of d... | https://arxiv.org/abs/2505.16850v1 |
Trustworthiness, and Reasoning, but also formalizes task settings, evaluation criteria, and research gaps in current literature. 2 Table 2: Overview of Key Attributes in Reviewed Techniques for Cross Calibration (See Β§ 2). Method Venue Core Idea Global Neural Network Drawback : Linear growth in local computational load... | https://arxiv.org/abs/2505.16850v1 |
future research directions and consolidate key datasets, tasks, and method trends to guide actionable progress in federated learning deployments. 2 Adaptive Federated Learning Adaptive Federated Learning tackles generalization and personalization across diverse clients. It balances global performance with client-specif... | https://arxiv.org/abs/2505.16850v1 |
Adds attention FedSB [164] [ICASSPβ25] Label smoothing and balanced training Careful tuning of smoothing parameters FedCGA [105] [ICMEβ24] Global consistent augmentation Assumes availability of diverse styles these strategies assume the availability of meaningful and appropriately matched auxiliary data, which may not ... | https://arxiv.org/abs/2505.16850v1 |
50]. In contrast, FDG seeks to train on heterogeneous client data and generalize directly to unseen domains, using either invariant optimization techniques, such as spectrum alignment [146], style normalization [ 12], barycenter-based feature fusion [ 233], or specialized architectural blocks (GANs [ 114], AdaIN [ 12],... | https://arxiv.org/abs/2505.16850v1 |
federated learn- ing, fair contribution evaluation is critical to reward clients in proportion to their inputs while respecting data privacy [ 112,110]. A common strategy is individualized evaluation, where each clientβs score is derived from locally available signals such as data acquisition cost [75], economic incent... | https://arxiv.org/abs/2505.16850v1 |
These inherently interpretable approaches, such as decision trees, fuzzy-rule systems, and knowledge graph embeddings, allow each client to build localized reasoning systems. The server merges symbolic components ( e.g., aligned rules or entity vectors), resulting in global models that are both transparent and adaptive... | https://arxiv.org/abs/2505.16850v1 |
metric details and Β§ E.3 for further insights. Cifar-10 Cifar-100 MNIST Fashion-MNISTMethods1.00.50.30.1E 1.00.50.30.1E 1.00.50.30.1E 1.00.50.30.1E FedAvg [118] 70.64 66.96 63.92 60.43 0.354 68.47 69.72 69.21 68.92 0.213 99.44 99.37 99.13 98.76 0.602 89.94 89.87 83.82 90.15 0.462 FedProx [173] 71.22 67.16 64.88 61.03 0... | https://arxiv.org/abs/2505.16850v1 |
accuracy over 100 communication rounds for various federated learning methods on Cifar-10, Cifar-100, MNIST, and Fashion-MNIST under a Dirichlet distribution with Ξ²= 0.5. Domain Skew & Out-Client Shift Datasets. Both Domain Skew and Out-Client Shift scenarios involve datasets originating from different domains, where t... | https://arxiv.org/abs/2505.16850v1 |
3.18 93.41 51.11 45.58 Bulyan [153] 46.88 44.06 20.68 10.00 10.00 54.88 82.62 80.76 9.21 78.00 73.57 15.12 97.01 98.18 1.23 93.21 92.13 7.19 93.21 92.13 4.57 86.04 87.20 9.49 Trim Median [35] 51.70 45.77 21.39 19.94 10.67 54.21 84.18 78.09 11.88 81.76 77.89 10.8 98.57 94.62 4.79 93.25 92.90 6.42 94.85 94.33 2.37 91.72 ... | https://arxiv.org/abs/2505.16850v1 |
60.00 56.45 8.43 87.81 87.72 2.25 87.26 87.66 1.03 99.31 98.97 0.44 98.78 98.85 0.47 95.73 94.60 2.10 95.31 94.28 2.41 FLTrust [192] / / / / / / / / / / / / 11.35 11.35 88.06 11.35 11.35 87.97 36.53 13.15 83.55 13.15 13.15 83.54 Sageflow [70] / / / / / / / / / / / / 98.76 96.75 2.66 93.14 89.85 9.47 92.40 78.20 18.50 8... | https://arxiv.org/abs/2505.16850v1 |
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