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Computation & Language / NLP
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Newest papers, page 175
Query-aware routing for Cross-lingual performance gains in Encoders
Multilingual encoders can exhibit reduced retrieval effectiveness when queries and relevant documents differ in language, despite strong same-language performance.Evaluating LLM-as-a-Judge Beyond Score Alignment: A Psychometric Analysis of Residual Judging Difficulty
Large language models (LLMs) are widely used as automatic judges, with validity typically assessed via alignment with human scores.Evaluating VQA in Vision Language Models using Cooperative Principles
We evaluate the performance of Vision Language Models in Visual Question Answering (VQA) when questions violate Grice's maxims.Misinformation Without Triggers: From Factual Answers to Downstream Decisions
Language models learn from web documents, some of them false, and false content can reach a model's answer to a factual question and the summaries and decisions that use it.Probe the Harness: Setup Checks for Stale-Data RL Comparisons in Language Models
Methods for training language models on stale samples are judged by comparisons against importance-corrected baselines.Output Language Confusion under Multilingual Prompt Contamination
Standard factual benchmarks assume clean monolingual prompts and exact-match scoring, two assumptions that break simultaneously in real-world multilingual deployment, from retrieval-augmented…Continual Graph Memory for Mathematical Research Agents
Using frontier agent harnesses to tackle mathematical research problems has emerged as an effective means of advancing mathematics.Enhancing Biomedical Named Entity Recognition via Multiple Programming Languages Instruction Tuning and Ensemble Method
Instruction tuning has become a common paradigm for applying large language models (LLMs) to biomedical named entity recognition (BioNER).Understanding Trajectory Heterogeneity in Federated World Model Learning
World models learn state evolution from trajectories, making access to temporal context a central training requirement.A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition
Large language models (LLMs) have shown promising potential for biomedical named entity recognition (BioNER) through instruction following and in-context learning.OLMo-Detect: A Multi-Stage, Confounder-Controlled Benchmark for Membership Inference on Large Language Models
Membership inference on large language models (LLMs) aims to determine whether a given text sample was included in an LLM's training data, without access to its training corpus.Sentry: Learning to Recover from LLM Agent Failures at Test Time
LLM agents often fail mid-task due to invalid tool calls, repeated actions, or poorly grounded reasoning, and learning from these failures is a path to reliability.OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination
Omni-modal large language models (OmniLLMs) unify text, images, audio, and video, yet hallucinate when generation relies on the wrong evidence.Recursive Self-Improvement in Unified Multimodal Models
Unified multimodal models (UMMs) understand and generate both text and images, which lets a model produce its own training data.Personalized Automatic Speech Recognition for a Dysarthric and Tracheostomic Speaker using Artificial Conversations
This work presents an automatic speech recognition (ASR) system personalized for a Czech speaker with a permanent tracheal stoma and severe dysarthria rendering their speech unintelligible to…ReSCUE: Re-translation with Sentence Commitment for Unsegmented Long-Form Simultaneous Sign Language Translation
Simultaneous Sign Language Translation (SLT) is critical for real-time communication, yet existing methods remain largely confined to sentence-level, offline settings that assume pre-segmented inputs.Verifiable, Articulable, and Tacit Components of Preference
What makes a short story gripping; a news article newsworthy; or a math proof elegant?Tailoring the Quantization Space for 1-Bit KV Cache Compression
The key-value (KV) cache becomes a major memory bottleneck in long-context LLM inference, placing substantial pressure on memory capacity and bandwidth.Adaptive Second-Order Solvers for Fast Stochastic Diffusion Sampling
Diffusion models rely on numerical solvers requiring time-discretization, which has a large influence on the tradeoff between sampling cost and quality.HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning
Long-form thinking traces can substantially improve the multi-step reasoning performance of large language models (LLMs), but they introduce high inference-time overhead, with latency dominated by…