1071 papers
This paper introduces CoPaLink, an automated approach that enhances bioinformatics workflow reproducibility by integrating Named Entity Recognition and entity linking to connect tool mentions in scientific papers with their corresponding implementations in executable workflow code.
This paper argues that current evaluations of attacks on PII removal techniques are flawed due to unmitigated data leakage and contamination, creating a paradox where trustworthy research requires access to private data that is inherently restricted from public scrutiny.
DualTurn is a dual-channel generative speech model that learns natural turn-taking dynamics through unsupervised pretraining on conversational audio and fine-tuning to predict agent actions, outperforming existing methods in both action prediction accuracy and turn-boundary anticipation while enabling tool-calling capabilities.
This paper introduces the Cross-Lingual Transfer Matrix (CLTM) to systematically quantify language-dependent performance variations in paralinguistic tasks like gender identification and speaker verification, revealing that despite their acoustic nature, these tasks exhibit distinct cross-lingual transfer patterns when using multilingual HuBERT-based encoders.
This paper introduces Fibration Policy Optimization (FiberPO), a unified framework that bridges trust-region theory and compositional algebraic structures to enable principled, multi-scale stability control in large language model training through the novel Aggregational Policy Censoring Objective and Fiber Bundle Gating mechanism.
This paper investigates how training randomness affects the stability of Transformer model explanations, demonstrating that while syntactic context, target classes, and task types all significantly influence this sensitivity, the impact is smallest for context, moderate for classes, and largest for tasks.
This paper proposes a zero-AV-resource framework for audiovisual speech recognition that generates synthetic talking-head videos by lip-syncing static facial images with real audio, successfully enabling high-performance model training for under-resourced languages like Catalan without the need for labeled video corpora.
The paper proposes CoFiCot, an adaptive coarse-to-fine framework that dynamically allocates test-time computation by triaging queries based on multi-metric difficulty assessment and applying stateful, context-aware refinement to balance efficiency and reasoning accuracy.
This paper presents the NCL-UoR system for SemEval-2026 Task 5, demonstrating that a structured prompting strategy with explicit decision rules for Large Language Models outperforms both embedding-based methods and fine-tuned transformers in rating word sense plausibility.
This study utilizes a massive 172-billion-token evaluation across diverse models, context lengths, and hardware to reveal that while model selection is the primary determinant of accuracy, hallucination rates in document Q&A rise significantly with context length and vary non-linearly with temperature, highlighting that grounding ability and fabrication resistance are distinct capabilities.
The paper proposes AdaCultureSafe, a framework that addresses the lack of correlation between cultural safety and knowledge in Large Language Models by constructing a novel dataset of culturally grounded queries and introducing a knowledge-integrated method to significantly enhance adaptive cultural safety.
This paper evaluates LLM-based grant proposal reviews using structured perturbations on six quality axes, finding that a section-by-section analysis approach outperforms other architectures but that current models still struggle with clarity detection and holistic assessment, suggesting they are best suited as supplementary tools rather than replacements for human reviewers.
This paper introduces SBARThez, a novel framework that leverages multimodal and multilingual sentence embeddings alongside a Named Entity Injection mechanism to enhance the factual consistency and cross-lingual capabilities of abstractive summarization for both text and speech inputs.
This paper introduces LAMUS, a large-scale, high-quality sentence-level legal argument mining corpus for U.S. caselaw constructed via an LLM-driven pipeline with human refinement, which demonstrates that chain-of-thought prompting and LLM-assisted verification significantly enhance annotation quality and model performance for future legal NLP research.
This paper proposes a unified post-training framework that extends speech foundation models to generate multiple arbitrary utterance-level attribute representations, demonstrating its effectiveness through the joint learning of semantic and speaker embeddings for multilingual retrieval and speaker recognition tasks.
This paper introduces SlowBA, a novel backdoor attack against VLM-based GUI agents that utilizes a two-stage reward-level injection strategy and realistic pop-up triggers to induce excessive reasoning chains, thereby significantly increasing response latency while maintaining task accuracy and evading existing defenses.
SPD-RAG is a hierarchical multi-agent framework that improves scalability and answer quality for complex cross-document queries by assigning dedicated agents to process individual documents and synthesizing their outputs through a token-bounded coordinator, achieving superior performance on the LOONG benchmark with significantly reduced API costs compared to standard RAG and full-context baselines.
This paper proposes replacing the dense output projection in multi-head attention with a parameter-free Walsh-Hadamard Transform and lightweight affine rescaling, achieving significant reductions in parameters, memory, and inference latency while maintaining or improving model performance across various benchmarks.
This paper introduces a diagnostic dataset and evaluation framework to investigate how language models handle the proviso problem in pragmatics, revealing that while models align with human judgments, they rely on shallow pattern matching rather than genuine semantic or pragmatic reasoning.
This chapter reviews recent computational models demonstrating that self-supervised and visually grounded learning principles can effectively explain early language acquisition from acoustic and audiovisual speech without relying on strong linguistic priors.