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cs.CL2026
Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
Xuan Qi, Rongwu Xu, Zhijing Jin
Aligning large language models (LLMs) with human preferences is a critical challenge in AI research. While methods like Reinforcement Learning from Human Feedback (RLHF) and Direct…
cs.CL2026
Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents
Xuan Qi
How much should a language agent think before taking action? Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning…
cs.CL2024
DebateQA: Evaluating Question Answering on Debatable Knowledge
Rongwu Xu, Xuan Qi, Zehan Qi +2
The rise of large language models (LLMs) has enabled us to seek answers to inherently debatable questions on LLM chatbots, necessitating a reliable way to evaluate their ability. H…