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cs.CL2026

W2S-AlignTree: Weak-to-Strong Inference-Time Alignment for Large Language Models via Monte Carlo Tree Search

Zhenyu Ding, Yuhao Wang, Tengyue Xiao +3

Large Language Models (LLMs) demonstrate impressive capabilities, yet their outputs often suffer from misalignment with human preferences due to the inadequacy of weak supervision…

cs.CL2025

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models

Ming Zhang, Yujiong Shen, Jingyi Deng +19

Existing evaluation of Large Language Models (LLMs) on static benchmarks is vulnerable to data contamination and leaderboard overfitting, critical issues that obscure true model ca…

cs.CL2025

SpeechRole: A Large-Scale Dataset and Benchmark for Evaluating Speech Role-Playing Agents

Changhao Jiang, Jiajun Sun, Yifei Cao +15

Speech is essential for realistic role-playing, yet existing work on role-playing agents largely centers on text, leaving Speech Role-Playing Agents (SRPAs) underexplored and witho…

cs.CL2025

Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs

Hao Fang, Changle Zhou, Jiawei Kong +3

Large Vision-Language Models (LVLMs) are susceptible to hallucinations, where generated responses seem semantically plausible yet exhibit little or no relevance to the input image.…

cs.CL2025

PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts

Ming Zhang, Yuhui Wang, Yujiong Shen +16

Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large La…

cs.CL2025

AnyEdit: Edit Any Knowledge Encoded in Language Models

Houcheng Jiang, Junfeng Fang, Ningyu Zhang +5

Large language models (LLMs) often produce incorrect or outdated information, necessitating efficient and precise knowledge updates. Current model editing methods, however, struggl…