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Computation & Language / NLP
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I'll Keep an Ear Out: Teaching AudioLLMs Proactive Audio Assistance
Audio large language models (AudioLLMs) operate reactively, responding only when queried.When Better Turns Do Not Make Better Agents: Diagnosing the Gap Between Next-Turn Metrics and Workflow Success
Agent models are frequently evaluated one decision at a time, where the model predicts the next action based on the gold interaction history, which is scored against a reference.Hallucination-R1: Robustness-Oriented Paraphrase Generation for Factual Consistency
Factual hallucination is commonly defined by incorrect factual outputs.Beyond Reference-Based Evaluation: Reward Models for Meta-Evaluation of Grammatical Error Correction
Reference-based metrics for Grammatical Error Correction (GEC) such as M$^2$ and ERRANT assume that the reference set enumerates all valid edits, and therefore often penalize corrections that are…When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces
Large Reasoning Models increasingly use intermediate traces for machine translation, but it remains unclear when such reasoning helps or hurts.How Many Humans Are 32 LLM Judges Worth?
A panel's human-equivalent size is target-specific.FairLMs: A Turnkey Library for Fairness in Language Models
Fairness research on language models involves measuring bias, applying mitigation methods, and examining the evidence on which an evaluation rests.Conformal Privacy Auditing: Calibrated Re-identification Attacks with Statistical Guarantees
Empirical identity leakage from released text is increasingly driven by attackers that combine large language models (LLMs) with auxiliary knowledge to link documents to individuals.From Memory to Behavior: A Behavior-Aware Role-Playing Framework for Social Media Influencers
Large language models have shown strong potential as role-playing agents for real individuals, yet faithful impersonating remains challenging.Beyond Atomic Tokens: Factorizing Syllables for Language Model Pretraining
Conventional tokenizers represent text as characters or statistically derived subwords, overlooking the internal phonological structure of syllables and often requiring large vocabularies.ArenaFlow: From Trajectory Ranking to Hierarchical Credit Propagation for Open-Ended Agent RL
Reinforcement learning has substantially improved large language model (LLM) agents in verifiable domains, but remains difficult to apply to open-ended agent tasks, where solutions are diverse and…Prediction Dynamics in Depth-Recurrent Language Models
Depth-recurrent language models refine predictions through repeated latent updates.Consistent Relexicalization of Clinical Documents using Graph-Based Approach
Relexicalization is a pivotal technique in clinical NLP, as it facilitates robust masking of sensitive information while synthesizing datasets that retain high-fidelity, real-world characteristics.Offline Multimodal Large Language Models for Decision Support in Air Operations
Air operations rely on complex rules, established procedures, and time-critical analysis under limited connectivity and strict security constraints.Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction
Natural audio-visual interaction is emerging as an important interface for AI assistants, allowing users to communicate through speech and vision rather than carefully composed text prompts.Talking Past the Machine: Morality, Politeness, and Alignment in Human-AI Dialogue
Conversational AI systems produce fluent, socially appropriate responses, yet whether they participate in cooperative communication or merely simulate its surface forms remains unclear - a question…Benchmarking Gender Bias in Machine Translation Evaluation Metrics across Occupations
Gender bias remains a persistent concern in machine translation (MT), affecting both generated translations and their automatic evaluation.MIRAGE: Multi-Perspective Creative Language Model Reasoning with Reinforcement Learning Guidance
Recent advances in Large Language Models (LLMs) have revolutionized artificial intelligence and how human interact with AIs.GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions
Coding agents can modify and test code across large software projects.Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction
In-context learning using Large Language Models (LLMs) offers a compelling path to training-free post-OCR correction, yet its effectiveness for Devanagari script remains entirely unexplored.