3 papers
cs.AI2025
Enhancing Reasoning through Process Supervision with Monte Carlo Tree Search
Shuangtao Li, Shuaihao Dong, Kexin Luan +2
Large language models (LLMs) have demonstrated their remarkable capacity across a variety of tasks. However, reasoning remains a challenge for LLMs. To improve LLMs' reasoning abil…
cs.CL2024
Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors
Mengge Xue, Zhenyu Hu, Liqun Liu +5
Multiple-Choice Questions (MCQs) constitute a critical area of research in the study of Large Language Models (LLMs). Previous works have investigated the selection bias problem in…
cs.CL2024
Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding
Kuo Liao, Shuang Li, Meng Zhao +5
Recent strides in large language models (LLMs) have yielded remarkable performance, leveraging reinforcement learning from human feedback (RLHF) to significantly enhance generation…