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Computation & Language / NLP
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The newest 5,000 papers, from 17 September 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 44
SimPOTrans: Self-Adaptation for Offline-to-Simultaneous Speech Translation via Reinforcement Learning
Simultaneous speech translation (SimulST) aims to translate ongoing speech with low latency while preserving translation quality.From Questions to Queries to Insights: A Schema-Grounded Agentic Workflow for Natural-Language Crash Data Analysis
Transportation agencies maintain extensive crash databases to identify safety priorities, evaluate countermeasures, and guide resource allocation.Reinforcement Learning Done Right for Explicit Confidence Targets
Large language models (LLMs) can produce plausible but incorrect answers when they are uncertain, which can mislead users and undermine model reliability.Improving VLMs on Scientific Chart Analysis via Autonomous Evolution
Understanding and operating with scientific charts is a standard approach in data analysis, and while humans are good at interpreting such charts, open vision-language models (VLMs) are known to…CoSA: The Conflict of Skills Attack in Real-World LLM Agent Ecosystems
Agent Skills allow LLM agents to combine reusable capabilities, but checking each Skill in isolation cannot rule out policy violations when they are composed.Ask Before You Answer: A Question-Driven Evidence Reasoning Architecture (QERA) for In-Context Learning
AI assistants are increasingly asked to reason over a user's own web traces: group chats, community threads, activity logs and revision histories.RAGing Against Misogyny: Improving Implicit Misogyny Explanations
Implicit misogyny often requires background knowledge to be understood.Useful Diversity or Hallucinated Variety? Persona-Conditioned Synthetic Query Generation for E-Commerce Search
Synthetic supervision can bootstrap semantic parsers in new search domains, but prompt-induced diversity is useful only when generations are faithful to the structured intent.Rethinking Evaluation for Principled Generalization Using a Kripkean Challenge to Meaning as Use
Large language models operationalize an intuitive, view of meaning as use.Quality over Quantity: Rethinking Data and Model Assumptions for Bengali Coreference Resolution
Multilingual transformer models are often assumed to generalise across languages, but their effectiveness for coreference resolution in low-resource settings remains unverified.CalibraRL: Confidence-Calibrated Reinforcement Learning for Mitigating Hallucinations in RAG
Large language models (LLMs) often produce plausible but incorrect answers, motivating reinforcement learning methods that reward correct answers, penalize incorrect answers, and allow abstention.Do Language Models Understand Igbo Culture? Beyond Multiple Choice
The value of language models for any community depends on whether a model actually understands the culture of the people it intends to serve.High Scores, Hidden Failures: Evaluating Codebook-Based LLM Content Analysis
Large language models (LLMs) are increasingly used for codebook-based content analysis in computational social science, where codebooks translate theoretical concepts into labeling schemes.Lost in Translation: Investigating Systematic Discrepancies between Parallel English and Chinese Names of American Chinese Restaurants
Given that the Chinese are one of the oldest ethnic groups to immigrate to the US, their cuisine have become central to the US dining scene and an interesting avenue to study how Chinese immigrants…DTCA: Denoising Token Credit Assignment for Reinforcement Learning in Diffusion Language Models
Diffusion language models (DLMs) generate text through iterative denoising, enabling bidirectional context modeling and parallel token refinement.Trainable Boundary-Keeping Where No Formal Answer Exists: A Controlled Knock-In Study of Reward Training in Evidence-Sufficiency Judgment
Reasoning-tuned and reward-trained models are often assumed to verify more reliably because they reason longer or receive correctness signals.Beyond Vision-Language Models: Visual-Semantic Affect Graphs for Emotion Recognition in Artworks
Emotion recognition in artworks remains challenging because affective meaning is often subjective, implicit, and shaped by relationships between visual form and semantic associations.Style over Substance? Linguistic Bias in LLM-based Peer Review Copy title
The number of paper submissions to major AI conferences has increased dramatically in recent years, substantially increasing the peer-review workload of researchers.The Lay of the Land in Idiom-Translation Quality Estimation
Idioms have long been a challenge for machine translation, and while large language models (LLMs) have proven increasingly adept at handling them, whether quality estimation (QE) systems have kept…MechAlign: Evaluating Semantic Alignment at the Project Level in Mathematical Formalization
Lean verifies that a formal proof establishes its stated proposition under declared assumptions, but does not check whether that proposition faithfully expresses the mathematical claim in a…