1061 papers
This paper proposes High-Fidelity Pruning (HFPrune), a method that utilizes the information entropy of a model's output distribution to evaluate neuron importance during Taylor-based pruning, thereby overcoming the limitations of standard cross-entropy criteria and the computational overhead of self-distillation to achieve superior performance on LLaMA and Qwen models without requiring an additional teacher model.
This paper introduces JudgeBiasBench, a comprehensive benchmark for systematically evaluating judgment biases across 12 types in both generative and discriminative LLM-based judges, and proposes a bias-aware training framework using reinforcement and contrastive learning to effectively mitigate these biases while preserving evaluation performance.
This paper introduces the Dual-Consensus Weak-to-Strong (DC-W2S) framework, which enhances the reliability of Process Reward Models in biological reasoning by strategically filtering noisy weak supervision signals through self- and neighborhood-consensus metrics to enable robust training without exhaustive expert annotation.
The paper introduces Ramsa, a 41-hour sociolinguistically diverse Emirati Arabic speech corpus comprising 157 speakers across various subdialects and topics, which serves as a foundational resource for developing low-resource ASR and TTS technologies while establishing initial performance baselines for existing models.
EvoScientist is an evolving multi-agent framework that leverages persistent memory and self-evolution to continuously refine research strategies, thereby outperforming existing state-of-the-art systems in generating novel scientific ideas and executing successful experiments for end-to-end discovery.
This paper proposes a gradual knowledge excavation framework that enables large language models to iteratively acquire external information and perform logical reasoning, achieving state-of-the-art performance on complex open-domain question answering with significantly fewer parameters.
This paper introduces two new benchmarks, WinoMTeus and FLORES+Gender, to evaluate gender bias in machine translation involving Basque, revealing that current large language models and MT systems exhibit a systematic preference for masculine forms when translating between genderless and gendered languages.
RexDrug is a reasoning-enhanced large language model framework that utilizes a two-stage training strategy combining multi-agent collaborative reasoning generation and reinforcement learning to achieve state-of-the-art performance in extracting complex n-ary drug combinations from biomedical literature.
This paper demonstrates that performing reasoning in a continuous latent space via the CODI framework significantly outperforms standard explicit reasoning in multilingual settings, particularly for low-resource and zero-shot scenarios, while achieving substantial compression of reasoning traces.
This paper introduces TildeOpen LLM, a 30-billion-parameter open-weight model that achieves superior performance across 34 European languages, particularly for low-resource groups, by employing curriculum learning and dataset upsampling to address data imbalances without requiring increased computational resources.
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.