id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.15238 | Dipper: Diversity in Prompts for Producing Large Language Model
Ensembles in Reasoning tasks | [
"cs.CL",
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"cs.MA"
] | Large Language Models still encounter substantial challenges in reasoning tasks, especially for smaller models, which many users may be restricted to due to resource constraints (e.g. GPU memory restrictions). Inference-time methods to boost LLM performance, such as prompting methods to invoke certain reasoning pathway... | {
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2412.15239 | Modeling Story Expectations to Understand Engagement: A Generative
Framework Using LLMs | [
"cs.CL",
"cs.AI",
"econ.GN",
"q-fin.EC",
"stat.ME"
] | Understanding when and why consumers engage with stories is crucial for content creators and platforms. While existing theories suggest that audience beliefs of what is going to happen should play an important role in engagement decisions, empirical work has mostly focused on developing techniques to directly extract f... | {
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2412.15240 | ChainStream: An LLM-based Framework for Unified Synthetic Sensing | [
"cs.CL",
"cs.AI",
"cs.SE"
] | Many applications demand context sensing to offer personalized and timely services. Yet, developing sensing programs can be challenging for developers and using them is privacy-concerning for end-users. In this paper, we propose to use natural language as the unified interface to process personal data and sense user co... | {
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2412.15241 | Quantifying Positional Biases in Text Embedding Models | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Embedding models are crucial for tasks in Information Retrieval (IR) and semantic similarity measurement, yet their handling of longer texts and associated positional biases remains underexplored. In this study, we investigate the impact of content position and input size on text embeddings. Our experiments reveal that... | {
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2412.15242 | Script-Based Dialog Policy Planning for LLM-Powered Conversational
Agents: A Basic Architecture for an "AI Therapist" | [
"cs.CL",
"cs.AI"
] | Large Language Model (LLM)-Powered Conversational Agents have the potential to provide users with scaled behavioral healthcare support, and potentially even deliver full-scale "AI therapy'" in the future. While such agents can already conduct fluent and proactive emotional support conversations, they inherently lack th... | {
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2412.15244 | MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary
Negative Samples | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Aligning Large Language Models (LLMs) with human feedback is crucial for their development. Existing preference optimization methods such as DPO and KTO, while improved based on Reinforcement Learning from Human Feedback (RLHF), are inherently derived from PPO, requiring a reference model that adds GPU memory resources... | {
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2412.15246 | Accelerating Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.AR",
"cs.DC",
"cs.IR"
] | An evolving solution to address hallucination and enhance accuracy in large language models (LLMs) is Retrieval-Augmented Generation (RAG), which involves augmenting LLMs with information retrieved from an external knowledge source, such as the web. This paper profiles several RAG execution pipelines and demystifies th... | {
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2412.15247 | Streamlining Systematic Reviews: A Novel Application of Large Language
Models | [
"cs.CL",
"cs.IR"
] | Systematic reviews (SRs) are essential for evidence-based guidelines but are often limited by the time-consuming nature of literature screening. We propose and evaluate an in-house system based on Large Language Models (LLMs) for automating both title/abstract and full-text screening, addressing a critical gap in the l... | {
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2412.15248 | RoundTripOCR: A Data Generation Technique for Enhancing Post-OCR Error
Correction in Low-Resource Devanagari Languages | [
"cs.CL",
"cs.CV"
] | Optical Character Recognition (OCR) technology has revolutionized the digitization of printed text, enabling efficient data extraction and analysis across various domains. Just like Machine Translation systems, OCR systems are prone to errors. In this work, we address the challenge of data generation and post-OCR error... | {
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2412.15249 | LLMs for Literature Review: Are we there yet? | [
"cs.CL",
"cs.AI",
"cs.DL",
"cs.LG"
] | Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially due to the recent influx of research papers. This paper explores the zero-shot abilities of recent Large Language Models (LLMs) in assisting with the writing of literature reviews ba... | {
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2412.15250 | An Enhanced Text Compression Approach Using Transformer-based Language
Models | [
"cs.CL",
"cs.IT",
"cs.LG",
"math.IT"
] | Text compression shrinks textual data while keeping crucial information, eradicating constraints on storage, bandwidth, and computational efficacy. The integration of lossless compression techniques with transformer-based text decompression has received negligible attention, despite the increasing volume of English tex... | {
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2412.15251 | AgentPS: Agentic Process Supervision for Multi-modal Content Quality
Assurance through Multi-round QA | [
"cs.CL",
"cs.AI"
] | The advanced processing and reasoning capabilities of multimodal large language models (MLLMs) have driven substantial progress in vision-language (VL) understanding tasks. However, while effective for tasks governed by straightforward logic, MLLMs often encounter challenges when reasoning over complex, interdependent ... | {
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2412.15252 | NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER)
within low-resource languages | [
"cs.CL",
"cs.AI"
] | Nowadays, Natural Language Processing (NLP) is an important tool for most people's daily life routines, ranging from understanding speech, translation, named entity recognition (NER), and text categorization, to generative text models such as ChatGPT. Due to the existence of big data and consequently large corpora for ... | {
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2412.15253 | Using Machine Learning to Distinguish Human-written from
Machine-generated Creative Fiction | [
"cs.CL",
"cs.LG"
] | Following the universal availability of generative AI systems with the release of ChatGPT, automatic detection of deceptive text created by Large Language Models has focused on domains such as academic plagiarism and "fake news". However, generative AI also poses a threat to the livelihood of creative writers, and perh... | {
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2412.15254 | RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of
Large Language Models in Data-Scarce Contexts | [
"cs.CL"
] | Large language models (LLMs) have significantly advanced natural language processing, excelling in areas like text generation, summarization, and question-answering. Despite their capabilities, these models face challenges when fine-tuned on small, domain-specific datasets, often struggling to generalize and deliver ac... | {
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2412.15255 | Data Laundering: Artificially Boosting Benchmark Results through
Knowledge Distillation | [
"cs.CL",
"cs.AI"
] | In this paper, we show that knowledge distillation can be subverted to manipulate language model benchmark scores, revealing a critical vulnerability in current evaluation practices. We introduce "Data Laundering," a three-phase process analogous to financial money laundering, that enables the covert transfer of benchm... | {
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2412.15256 | Structured Extraction of Real World Medical Knowledge using LLMs for
Summarization and Search | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Creation and curation of knowledge graphs can accelerate disease discovery and analysis in real-world data. While disease ontologies aid in biological data annotation, codified categories (SNOMED-CT, ICD10, CPT) may not capture patient condition nuances or rare diseases. Multiple disease definitions across data sources... | {
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2412.15257 | An Incremental Clustering Baseline for Event Detection on Twitter | [
"cs.CL",
"cs.SI"
] | Event detection in text streams is a crucial task for the analysis of online media and social networks. One of the current challenges in this field is establishing a performance standard while maintaining an acceptable level of computational complexity. In our study, we use an incremental clustering algorithm combined ... | {
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2412.15258 | DisEmbed: Transforming Disease Understanding through Embeddings | [
"cs.CL",
"cs.LG"
] | The medical domain is vast and diverse, with many existing embedding models focused on general healthcare applications. However, these models often struggle to capture a deep understanding of diseases due to their broad generalization across the entire medical field. To address this gap, I present DisEmbed, a disease-f... | {
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2412.15259 | GLARE: Google Apps Arabic Reviews Dataset | [
"cs.CL",
"cs.SI"
] | This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the ga... | {
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2412.15260 | Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access
to Justice | [
"cs.CL",
"cs.CV",
"cs.MM"
] | Interacting with the legal system and the government requires the assembly and analysis of various pieces of information that can be spread across different (paper) documents, such as forms, certificates and contracts (e.g. leases). This information is required in order to understand one's legal rights, as well as to f... | {
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2412.15262 | Advanced ingestion process powered by LLM parsing for RAG system | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Retrieval Augmented Generation (RAG) systems struggle with processing multimodal documents of varying structural complexity. This paper introduces a novel multi-strategy parsing approach using LLM-powered OCR to extract content from diverse document types, including presentations and high text density files both scanne... | {
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2412.15263 | PROPOE 2: Avan\c{c}os na S\'intese Computacional de Poemas Baseados em
Prosa Liter\'aria Brasileira | [
"cs.CL"
] | The computational generation of poems is a complex task, which involves several sound, prosodic and rhythmic resources. In this work we present PROPOE 2, with the extension of structural and rhythmic possibilities compared to the original system, generating poems from metered sentences extracted from the prose of Brazi... | {
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2412.15264 | ReXTrust: A Model for Fine-Grained Hallucination Detection in
AI-Generated Radiology Reports | [
"cs.CL",
"cs.AI"
] | The increasing adoption of AI-generated radiology reports necessitates robust methods for detecting hallucinations--false or unfounded statements that could impact patient care. We present ReXTrust, a novel framework for fine-grained hallucination detection in AI-generated radiology reports. Our approach leverages sequ... | {
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2412.15265 | Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large
Language Models | [
"cs.CL",
"cs.AI"
] | With the rapid advancement of Large Language Models (LLMs), significant safety concerns have emerged. Fundamentally, the safety of large language models is closely linked to the accuracy, comprehensiveness, and clarity of their understanding of safety knowledge, particularly in domains such as law, policy and ethics. T... | {
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2412.15266 | On the Structural Memory of LLM Agents | [
"cs.CL",
"cs.AI"
] | Memory plays a pivotal role in enabling large language model~(LLM)-based agents to engage in complex and long-term interactions, such as question answering (QA) and dialogue systems. While various memory modules have been proposed for these tasks, the impact of different memory structures across tasks remains insuffici... | {
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2412.15267 | Toxicity Detection towards Adaptability to Changing Perturbations | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.LG"
] | Toxicity detection is crucial for maintaining the peace of the society. While existing methods perform well on normal toxic contents or those generated by specific perturbation methods, they are vulnerable to evolving perturbation patterns. However, in real-world scenarios, malicious users tend to create new perturbati... | {
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2412.15268 | Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic
Knowledge Graph | [
"cs.CL",
"cs.AI"
] | The rapid growth of social media platforms has raised significant concerns regarding online content toxicity. When Large Language Models (LLMs) are used for toxicity detection, two key challenges emerge: 1) the absence of domain-specific toxic knowledge leads to false negatives; 2) the excessive sensitivity of LLMs to ... | {
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2412.15269 | The Reliability Paradox: Exploring How Shortcut Learning Undermines
Language Model Calibration | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The advent of pre-trained language models (PLMs) has enabled significant performance gains in the field of natural language processing. However, recent studies have found PLMs to suffer from miscalibration, indicating a lack of accuracy in the confidence estimates provided by these models. Current evaluation methods fo... | {
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2412.15270 | Baichuan4-Finance Technical Report | [
"cs.CL",
"cs.AI",
"cs.CE",
"cs.CY",
"cs.LG"
] | Large language models (LLMs) have demonstrated strong capabilities in language understanding, generation, and reasoning, yet their potential in finance remains underexplored due to the complexity and specialization of financial knowledge. In this work, we report the development of the Baichuan4-Finance series, includin... | {
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2412.15271 | A MapReduce Approach to Effectively Utilize Long Context Information in
Retrieval Augmented Language Models | [
"cs.CL",
"cs.IR"
] | While holding great promise for improving and facilitating healthcare, large language models (LLMs) struggle to produce up-to-date responses on evolving topics due to outdated knowledge or hallucination. Retrieval-augmented generation (RAG) is a pivotal innovation that improves the accuracy and relevance of LLM respons... | {
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2412.15272 | SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven
Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Recent advancements in large language models (LLMs) have shown impressive versatility across various tasks. To eliminate its hallucinations, retrieval-augmented generation (RAG) has emerged as a powerful approach, leveraging external knowledge sources like knowledge graphs (KGs). In this paper, we study the task of KG-... | {
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2412.15273 | Do Voters Get the Information They Want? Understanding Authentic Voter
FAQs in the US and How to Improve for Informed Electoral Participation | [
"cs.CY",
"cs.CL"
] | Accurate information is crucial for democracy as it empowers voters to make informed decisions about their representatives and keeping them accountable. In the US, state election commissions (SECs), often required by law, are the primary providers of Frequently Asked Questions (FAQs) to voters, and secondary sources li... | {
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2412.15274 | Memory-Augmented Agent Training for Business Document Understanding | [
"cs.CL",
"cs.AI"
] | Traditional enterprises face significant challenges in processing business documents, where tasks like extracting transport references from invoices remain largely manual despite their crucial role in logistics operations. While Large Language Models offer potential automation, their direct application to specialized b... | {
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2412.15275 | Fooling LLM graders into giving better grades through neural activity
guided adversarial prompting | [
"cs.CR",
"cs.AI",
"cs.CL"
] | The deployment of artificial intelligence (AI) in critical decision-making and evaluation processes raises concerns about inherent biases that malicious actors could exploit to distort decision outcomes. We propose a systematic method to reveal such biases in AI evaluation systems and apply it to automated essay gradin... | {
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2412.15276 | Exploring Query Efficient Data Generation towards Data-free Model
Stealing in Hard Label Setting | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Data-free model stealing involves replicating the functionality of a target model into a substitute model without accessing the target model's structure, parameters, or training data. The adversary can only access the target model's predictions for generated samples. Once the substitute model closely approximates the b... | {
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2412.15277 | PLPP: Prompt Learning with Perplexity Is Self-Distillation for
Vision-Language Models | [
"cs.CL",
"cs.AI"
] | Pre-trained Vision-Language (VL) models such as CLIP have demonstrated their excellent performance across numerous downstream tasks. A recent method, Context Optimization (CoOp), further improves the performance of VL models on downstream tasks by introducing prompt learning. CoOp optimizes a set of learnable vectors, ... | {
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2412.15278 | DreaMark: Rooting Watermark in Score Distillation Sampling Generated
Neural Radiance Fields | [
"cs.GR",
"cs.AI"
] | Recent advancements in text-to-3D generation can generate neural radiance fields (NeRFs) with score distillation sampling, enabling 3D asset creation without real-world data capture. With the rapid advancement in NeRF generation quality, protecting the copyright of the generated NeRF has become increasingly important. ... | {
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2412.15279 | Functional connectomes of neural networks | [
"cs.NE",
"cs.AI",
"cs.LG",
"q-bio.NC"
] | The human brain is a complex system, and understanding its mechanisms has been a long-standing challenge in neuroscience. The study of the functional connectome, which maps the functional connections between different brain regions, has provided valuable insights through various advanced analysis techniques developed o... | {
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2412.15280 | Context-DPO: Aligning Language Models for Context-Faithfulness | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information. While alignment techniques help LLMs align with human intentions and values, improving context-faithfulness through alignment remains underexplored. To address this, we propose $\textbf{Context-DPO}$, ... | {
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2412.15282 | A Systematic Examination of Preference Learning through the Lens of
Instruction-Following | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Preference learning is a widely adopted post-training technique that aligns large language models (LLMs) to human preferences and improves specific downstream task capabilities. In this work we systematically investigate how specific attributes of preference datasets affect the alignment and downstream performance of L... | {
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2412.15283 | Channel Merging: Preserving Specialization for Merged Experts | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Lately, the practice of utilizing task-specific fine-tuning has been implemented to improve the performance of large language models (LLM) in subsequent tasks. Through the integration of diverse LLMs, the overall competency of LLMs is significantly boosted. Nevertheless, traditional ensemble methods are notably memory-... | {
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2412.15285 | Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase
Pretraining | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Pretraining large language models effectively requires strategic data selection, blending and ordering. However, key details about data mixtures especially their scalability to longer token horizons and larger model sizes remain underexplored due to limited disclosure by model developers. To address this, we formalize ... | {
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2412.15287 | Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language
Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Recent studies have indicated that effectively utilizing inference-time compute is crucial for attaining better performance from large language models (LLMs). In this work, we propose a novel inference-aware fine-tuning paradigm, in which the model is fine-tuned in a manner that directly optimizes the performance of th... | {
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2412.15289 | SATA: A Paradigm for LLM Jailbreak via Simple Assistive Task Linkage | [
"cs.CR",
"cs.AI",
"cs.CL"
] | Large language models (LLMs) have made significant advancements across various tasks, but their safety alignment remain a major concern. Exploring jailbreak prompts can expose LLMs' vulnerabilities and guide efforts to secure them. Existing methods primarily design sophisticated instructions for the LLM to follow, or r... | {
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2412.15291 | A Large-scale Empirical Study on Large Language Models for Election
Prediction | [
"cs.CL",
"cs.SI"
] | Can Large Language Models (LLMs) accurately predict election outcomes? While LLMs have demonstrated impressive performance in healthcare, legal analysis, and creative applications, their capabilities in election forecasting remain uncertain. Notably, election prediction poses unique challenges: limited voter-level data... | {
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2412.15292 | Deep reinforcement learning with time-scale invariant memory | [
"cs.AI",
"cs.LG"
] | The ability to estimate temporal relationships is critical for both animals and artificial agents. Cognitive science and neuroscience provide remarkable insights into behavioral and neural aspects of temporal credit assignment. In particular, scale invariance of learning dynamics, observed in behavior and supported by ... | {
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2412.15294 | A Universal Model for Human Mobility Prediction | [
"cs.LG",
"cs.AI"
] | Predicting human mobility is crucial for urban planning, traffic control, and emergency response. Mobility behaviors can be categorized into individual and collective, and these behaviors are recorded by diverse mobility data, such as individual trajectory and crowd flow. As different modalities of mobility data, indiv... | {
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2412.15295 | Log-Time K-Means Clustering for 1D Data: Novel Approaches with Proof and
Implementation | [
"cs.DS",
"cs.LG"
] | Clustering is a key task in machine learning, with $k$-means being widely used for its simplicity and effectiveness. While 1D clustering is common, existing methods often fail to exploit the structure of 1D data, leading to inefficiencies. This thesis introduces optimized algorithms for $k$-means++ initialization and L... | {
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2412.15296 | Confidence in the Reasoning of Large Language Models | [
"cs.CL",
"cs.LG"
] | There is a growing literature on reasoning by large language models (LLMs), but the discussion on the uncertainty in their responses is still lacking. Our aim is to assess the extent of confidence that LLMs have in their answers and how it correlates with accuracy. Confidence is measured (i) qualitatively in terms of p... | {
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2412.15298 | A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large
Language Models Evaluation Metrics to Human Evaluation | [
"cs.CL",
"cs.AI",
"cs.LG",
"q-fin.ST",
"stat.ME"
] | We argue that the Declarative Self-improving Python (DSPy) optimizers are a way to align the large language model (LLM) prompts and their evaluations to the human annotations. We present a comparative analysis of five teleprompter algorithms, namely, Cooperative Prompt Optimization (COPRO), Multi-Stage Instruction Prom... | {
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2412.15299 | LAMA-UT: Language Agnostic Multilingual ASR through Orthography
Unification and Language-Specific Transliteration | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Building a universal multilingual automatic speech recognition (ASR) model that performs equitably across languages has long been a challenge due to its inherent difficulties. To address this task we introduce a Language-Agnostic Multilingual ASR pipeline through orthography Unification and language-specific Transliter... | {
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2412.15301 | Parametric $\rho$-Norm Scaling Calibration | [
"cs.LG",
"cs.CV"
] | Output uncertainty indicates whether the probabilistic properties reflect objective characteristics of the model output. Unlike most loss functions and metrics in machine learning, uncertainty pertains to individual samples, but validating it on individual samples is unfeasible. When validated collectively, it cannot f... | {
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2412.15302 | Tokenphormer: Structure-aware Multi-token Graph Transformer for Node
Classification | [
"cs.LG"
] | Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoothing and over-squashing limit the receptive field in message passing processes. Graph Transformers ... | {
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2412.15303 | Self-Evolution Knowledge Distillation for LLM-based Machine Translation | [
"cs.CL"
] | Knowledge distillation (KD) has shown great promise in transferring knowledge from larger teacher models to smaller student models. However, existing KD strategies for large language models often minimize output distributions between student and teacher models indiscriminately for each token. This overlooks the imbalan... | {
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2412.15304 | TinyLLM: A Framework for Training and Deploying Language Models at the
Edge Computers | [
"cs.LG",
"cs.DC",
"cs.ET",
"cs.NI"
] | Language models have gained significant interest due to their general-purpose capabilities, which appear to emerge as models are scaled to increasingly larger parameter sizes. However, these large models impose stringent requirements on computing systems, necessitating significant memory and processing requirements for... | {
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2412.15305 | Tree-of-Code: A Tree-Structured Exploring Framework for End-to-End Code
Generation and Execution in Complex Task Handling | [
"cs.SE",
"cs.AI"
] | Solving complex reasoning tasks is a key real-world application of agents. Thanks to the pretraining of Large Language Models (LLMs) on code data, recent approaches like CodeAct successfully use code as LLM agents' action, achieving good results. However, CodeAct greedily generates the next action's code block by relyi... | {
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2412.15306 | MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted
Traffic Classification | [
"cs.CR",
"cs.LG"
] | Network traffic includes data transmitted across a network, such as web browsing and file transfers, and is organized into packets (small units of data) and flows (sequences of packets exchanged between two endpoints). Classifying encrypted traffic is essential for detecting security threats and optimizing network mana... | {
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2412.15307 | Federated Learning for Coronary Artery Plaque Detection in
Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration | [
"eess.IV",
"cs.CV"
] | The traditional interpretation of Intravascular Ultrasound (IVUS) images during Percutaneous Coronary Intervention (PCI) is time-intensive and inconsistent, relying heavily on physician expertise. Regulatory restrictions and privacy concerns further hinder data integration across hospital systems, complicating collabor... | {
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2412.15308 | ViFactCheck: A New Benchmark Dataset and Methods for Multi-domain News
Fact-Checking in Vietnamese | [
"cs.CL",
"cs.IR"
] | The rapid spread of information in the digital age highlights the critical need for effective fact-checking tools, particularly for languages with limited resources, such as Vietnamese. In response to this challenge, we introduce ViFactCheck, the first publicly available benchmark dataset designed specifically for Viet... | {
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2412.15309 | Conceptual In-Context Learning and Chain of Concepts: Solving Complex
Conceptual Problems Using Large Language Models | [
"cs.CL",
"cs.AI"
] | Science and engineering problems fall in the category of complex conceptual problems that require specific conceptual information (CI) like math/logic -related know-how, process information, or engineering guidelines to solve them. Large Language Models (LLMs) are promising agents to solve such complex conceptual probl... | {
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2412.15310 | MRWeb: An Exploration of Generating Multi-Page Resource-Aware Web Code
from UI Designs | [
"cs.SE",
"cs.AI",
"cs.IR"
] | Multi-page websites dominate modern web development. However, existing design-to-code methods rely on simplified assumptions, limiting to single-page, self-contained webpages without external resource connection. To address this gap, we introduce the Multi-Page Resource-Aware Webpage (MRWeb) generation task, which tran... | {
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2412.15311 | Re-evaluating Group Robustness via Adaptive Class-Specific Scaling | [
"cs.LG"
] | Group distributionally robust optimization, which aims to improve robust accuracies -- worst-group and unbiased accuracies -- is a prominent algorithm used to mitigate spurious correlations and address dataset bias. Although existing approaches have reported improvements in robust accuracies, these gains often come at ... | {
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2412.15312 | PCA-Featured Transformer for Jamming Detection in 5G UAV Networks | [
"cs.LG"
] | Jamming attacks pose a threat to Unmanned Aerial Vehicle (UAV) wireless communication systems, potentially disrupting essential services and compromising network reliability. Current detection approaches struggle with sophisticated artificial intelligence (AI) jamming techniques that adapt their patterns while existing... | {
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2412.15314 | Eliciting Causal Abilities in Large Language Models for Reasoning Tasks | [
"cs.CL",
"cs.AI"
] | Prompt optimization automatically refines prompting expressions, unlocking the full potential of LLMs in downstream tasks. However, current prompt optimization methods are costly to train and lack sufficient interpretability. This paper proposes enhancing LLMs' reasoning performance by eliciting their causal inference ... | {
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2412.15315 | Enhancing Masked Time-Series Modeling via Dropping Patches | [
"stat.ML",
"cs.LG"
] | This paper explores how to enhance existing masked time-series modeling by randomly dropping sub-sequence level patches of time series. On this basis, a simple yet effective method named DropPatch is proposed, which has two remarkable advantages: 1) It improves the pre-training efficiency by a square-level advantage; 2... | {
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2412.15320 | Multi-concept Model Immunization through Differentiable Model Merging | [
"cs.CV"
] | Model immunization is an emerging direction that aims to mitigate the potential risk of misuse associated with open-sourced models and advancing adaptation methods. The idea is to make the released models' weights difficult to fine-tune on certain harmful applications, hence the name ``immunized''. Recent work on model... | {
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2412.15321 | Next Patch Prediction for Autoregressive Visual Generation | [
"cs.CV"
] | Autoregressive models, built based on the Next Token Prediction (NTP) paradigm, show great potential in developing a unified framework that integrates both language and vision tasks. In this work, we rethink the NTP for autoregressive image generation and propose a novel Next Patch Prediction (NPP) paradigm. Our key id... | {
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2412.15322 | Taming Multimodal Joint Training for High-Quality Video-to-Audio
Synthesis | [
"cs.CV",
"cs.LG",
"cs.SD",
"eess.AS"
] | We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework MMAudio. In contrast to single-modality training conditioned on (limited) video data only, MMAudio is jointly trained with larger-scale, readily available text-audio ... | {
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2412.15337 | A Hybrid Three-Port Topology for Urban Charging Stations | [
"eess.SY",
"cs.SY"
] | Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, the available multiport topologies have variable switching frequencies. This paper introduces a hybrid ... | {
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2412.15341 | Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion
Models | [
"cs.LG",
"cs.CV"
] | Recent advances in diffusion generative models have yielded remarkable progress. While the quality of generated content continues to improve, these models have grown considerably in size and complexity. This increasing computational burden poses significant challenges, particularly in resource-constrained deployment sc... | {
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2412.15342 | DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal
Cardiac MRI | [
"eess.IV",
"cs.CV"
] | Dynamic fetal heart magnetic resonance imaging (MRI) presents unique challenges due to the fast heart rate of the fetus compared to adult subjects and uncontrolled fetal motion. This requires high temporal and spatial resolutions over a large field of view, in order to encompass surrounding maternal anatomy. In this wo... | {
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2412.15347 | Exploring Machine Learning Engineering for Object Detection and Tracking
by Unmanned Aerial Vehicle (UAV) | [
"cs.CV",
"cs.AI",
"cs.LG"
] | With the advancement of deep learning methods it is imperative that autonomous systems will increasingly become intelligent with the inclusion of advanced machine learning algorithms to execute a variety of autonomous operations. One such task involves the design and evaluation for a subsystem of the perception system ... | {
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2412.15348 | Autonomous Vehicle Security: A Deep Dive into Threat Modeling | [
"eess.SY",
"cs.SY"
] | Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, the increasing complexity and connectivity of AV systems introduce significant cybersecurity challenges. This paper provides a comprehensive survey of AV security with a focus on ... | {
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2412.15349 | Adaptive Urban Planning: A Hybrid Framework for Balanced City
Development | [
"cs.MA",
"cs.LG"
] | Urban planning faces a critical challenge in balancing city-wide infrastructure needs with localized demographic preferences, particularly in rapidly developing regions. Although existing approaches typically focus on top-down optimization or bottom-up community planning, only some frameworks successfully integrate bot... | {
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2412.15352 | Large Language Models on Small Resource-Constrained Systems: Performance
Characterization, Analysis and Trade-offs | [
"cs.LG",
"cs.CC"
] | Generative AI like the Large Language Models (LLMs) has become more available for the general consumer in recent years. Publicly available services, e.g., ChatGPT, perform token generation on networked cloud server hardware, effectively removing the hardware entry cost for end users. However, the reliance on network ac... | {
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2412.15353 | GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models
through Statistically-Guided Geo-Prototyping | [
"cs.LG",
"cs.AI"
] | The problem of forecasting spatiotemporal events such as crimes and accidents is crucial to public safety and city management. Besides accuracy, interpretability is also a key requirement for spatiotemporal forecasting models to justify the decisions. Interpretation of the spatiotemporal forecasting mechanism is, howev... | {
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2412.15358 | Dataset Augmentation by Mixing Visual Concepts | [
"cs.CV"
] | This paper proposes a dataset augmentation method by fine-tuning pre-trained diffusion models. Generating images using a pre-trained diffusion model with textual conditioning often results in domain discrepancy between real data and generated images. We propose a fine-tuning approach where we adapt the diffusion model ... | {
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2412.15360 | Decade of Natural Language Processing in Chronic Pain: A Systematic
Review | [
"cs.CL"
] | In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various domains, including chronic pain in textual datasets. Despite the promise of NLP in chronic pain, the literature is dispersed across various disciplines, and there is a need t... | {
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2412.15361 | A Generative Framework for Probabilistic, Spatiotemporally Coherent
Downscaling of Climate Simulation | [
"cs.LG",
"physics.ao-ph"
] | Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical downscaling methods infer these phenomena as temporally decoupled spatial patches. However, to preserve physical properties, estimating spatio-t... | {
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2412.15363 | Making Transparency Advocates: An Educational Approach Towards Better
Algorithmic Transparency in Practice | [
"cs.CY",
"cs.AI",
"cs.HC"
] | Concerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been ... | {
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2412.15365 | LISA: Learning-Integrated Space Partitioning Framework for Traffic
Accident Forecasting on Heterogeneous Spatiotemporal Data | [
"cs.LG"
] | Traffic accident forecasting is an important task for intelligent transportation management and emergency response systems. However, this problem is challenging due to the spatial heterogeneity of the environment. Existing data-driven methods mostly focus on studying homogeneous areas with limited size (e.g. a single u... | {
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2412.15366 | Capacity and PAPR Analysis for MIMO Faster-than-Nyquist Signaling with
High Acceleration | [
"cs.IT",
"math.IT"
] | Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique offering a promising solution for future generations of communications. This paper studies the capacity of FTN signaling in multiple-input multiple-output (MIMO) channels for high acceleration factors. In our previous study [1], we found the... | {
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2412.15369 | Scalable and low-cost remote lab platforms: Teaching industrial robotics
using open-source tools and understanding its social implications | [
"cs.RO"
] | With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training m... | {
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2412.15372 | A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations | [
"cs.LG"
] | Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inputs. Data-driven networks like GNN, Neural Operators have proved to be very effective in generalizing the model across unseen domain and reso... | {
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2412.15373 | Granger Causality Detection with Kolmogorov-Arnold Networks | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Discovering causal relationships in time series data is central in many scientific areas, ranging from economics to climate science. Granger causality is a powerful tool for causality detection. However, its original formulation is limited by its linear form and only recently nonlinear machine-learning generalizations ... | {
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2412.15374 | Automated Root Cause Analysis System for Complex Data Products | [
"cs.SE",
"cs.AI"
] | We present ARCAS (Automated Root Cause Analysis System), a diagnostic platform based on a Domain Specific Language (DSL) built for fast diagnostic implementation and low learning curve. Arcas is composed of a constellation of automated troubleshooting guides (Auto-TSGs) that can execute in parallel to detect issues usi... | {
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2412.15375 | Automatic Extraction of Metaphoric Analogies from Literary Texts: Task
Formulation, Dataset Construction, and Evaluation | [
"cs.CL"
] | Extracting metaphors and analogies from free text requires high-level reasoning abilities such as abstraction and language understanding. Our study focuses on the extraction of the concepts that form metaphoric analogies in literary texts. To this end, we construct a novel dataset in this domain with the help of domain... | {
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2412.15380 | Uncertainty-Guided Cross Attention Ensemble Mean Teacher for
Semi-supervised Medical Image Segmentation | [
"cs.CV"
] | This work proposes a novel framework, Uncertainty-Guided Cross Attention Ensemble Mean Teacher (UG-CEMT), for achieving state-of-the-art performance in semi-supervised medical image segmentation. UG-CEMT leverages the strengths of co-training and knowledge distillation by combining a Cross-attention Ensemble Mean Teach... | {
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2412.15386 | Systematic Evaluation of Long-Context LLMs on Financial Concepts | [
"cs.CL",
"cs.AI"
] | Long-context large language models (LC LLMs) promise to increase reliability of LLMs in real-world tasks requiring processing and understanding of long input documents. However, this ability of LC LLMs to reliably utilize their growing context windows remains under investigation. In this work, we evaluate the performan... | {
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} |
2412.15388 | Investigating Relational State Abstraction in Collaborative MARL | [
"cs.AI",
"cs.LG",
"cs.MA"
] | This paper explores the impact of relational state abstraction on sample efficiency and performance in collaborative Multi-Agent Reinforcement Learning. The proposed abstraction is based on spatial relationships in environments where direct communication between agents is not allowed, leveraging the ubiquity of spatial... | {
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} |
2412.15389 | Maximising Histopathology Segmentation using Minimal Labels via
Self-Supervision | [
"cs.CV"
] | Histopathology, the microscopic examination of tissue samples, is essential for disease diagnosis and prognosis. Accurate segmentation and identification of key regions in histopathology images are crucial for developing automated solutions. However, state-of-art deep learning segmentation methods like UNet require ext... | {
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} |
2412.15392 | Leveraging Weak Supervision for Cell Localization in Digital Pathology
Using Multitask Learning and Consistency Loss | [
"eess.IV",
"cs.CV"
] | Cell detection and segmentation are integral parts of automated systems in digital pathology. Encoder-decoder networks have emerged as a promising solution for these tasks. However, training of these networks has typically required full boundary annotations of cells, which are labor-intensive and difficult to obtain on... | {
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} |
2412.15396 | Learning Visual Composition through Improved Semantic Guidance | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.IR"
] | Visual imagery does not consist of solitary objects, but instead reflects the composition of a multitude of fluid concepts. While there have been great advances in visual representation learning, such advances have focused on building better representations for a small number of discrete objects bereft of an understand... | {
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} |
2412.15398 | Tabletop Object Rearrangement: Structure, Complexity, and Efficient
Combinatorial Search-Based Solutions | [
"cs.RO"
] | This thesis provides an in-depth structural analysis and efficient algorithmic solutions for tabletop object rearrangement with overhand grasps (TORO), a foundational task in advancing intelligent robotic manipulation. Rearranging multiple objects in a confined workspace presents two primary challenges: sequencing acti... | {
"Other": 0,
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} |
2412.15400 | SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface
Reconstruction | [
"cs.CV"
] | Gaussian splatting has achieved impressive improvements for both novel-view synthesis and surface reconstruction from multi-view images. However, current methods still struggle to reconstruct high-quality surfaces from only sparse view input images using Gaussian splatting. In this paper, we propose a novel method call... | {
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} |
2412.15402 | Optimizing Photovoltaic Panel Quantity for Water Distribution Networks | [
"eess.SY",
"cs.SY"
] | The paper introduces a procedure for determining an approximation of the optimal amount of photovoltaics (PVs) for powering water distribution networks (WDNs) through grid-connected PVs. The procedure aims to find the PV amount minimizing the total expected cost of the WDN over the lifespan of the PVs. The approach fol... | {
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"cs.SY": 1
} |
2412.15404 | A Retrieval-Augmented Generation Framework for Academic Literature
Navigation in Data Science | [
"cs.IR"
] | In the rapidly evolving field of data science, efficiently navigating the expansive body of academic literature is crucial for informed decision-making and innovation. This paper presents an enhanced Retrieval-Augmented Generation (RAG) application, an artificial intelligence (AI)-based system designed to assist data s... | {
"Other": 0,
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} |
2412.15405 | Cosmology with Persistent Homology: Parameter Inference via Machine
Learning | [
"astro-ph.CO",
"cs.LG"
] | Building upon [2308.02636], this article investigates the potential constraining power of persistent homology for cosmological parameters and primordial non-Gaussianity amplitudes in a likelihood-free inference pipeline. We evaluate the ability of persistence images (PIs) to infer parameters, compared to the combined P... | {
"Other": 0,
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} |
2412.15410 | Deciphering Social Behaviour: a Novel Biological Approach For Social
Users Classification | [
"cs.SI"
] | Social media platforms continue to struggle with the growing presence of social bots-automated accounts that can influence public opinion and facilitate the spread of disinformation. Over time, these social bots have advanced significantly, making them increasingly difficult to distinguish from genuine users. Recently,... | {
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} |
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