Chain of Thought
Chain of Thought (CoT) prompting enhances the reasoning abilities of large language models (LLMs) by encouraging them to generate intermediate reasoning steps before arriving at a final answer. Current research focuses on improving CoT's effectiveness through techniques like multi-perspective verification, incorporating external knowledge (e.g., symbolic knowledge or multi-modal information), and optimizing the efficiency of the reasoning process (e.g., through compressed representations or adaptive sampling). This work is significant because it addresses limitations in LLMs' reasoning capabilities, leading to improved performance on complex tasks across diverse domains, including question answering, translation, and even medical diagnosis.
Papers
MC-CoT: A Modular Collaborative CoT Framework for Zero-shot Medical-VQA with LLM and MLLM Integration
Lai Wei, Wenkai Wang, Xiaoyu Shen, Yu Xie, Zhihao Fan, Xiaojin Zhang, Zhongyu Wei, Wei Chen
Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information
Yongheng Zhang, Qiguang Chen, Jingxuan Zhou, Peng Wang, Jiasheng Si, Jin Wang, Wenpeng Lu, Libo Qin
Understanding Reasoning in Chain-of-Thought from the Hopfieldian View
Lijie Hu, Liang Liu, Shu Yang, Xin Chen, Zhen Tan, Muhammad Asif Ali, Mengdi Li, Di Wang
Visual-O1: Understanding Ambiguous Instructions via Multi-modal Multi-turn Chain-of-thoughts Reasoning
Minheng Ni, Yutao Fan, Lei Zhang, Wangmeng Zuo
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning
Tianxiang Hu, Pei Zhang, Baosong Yang, Jun Xie, Derek F. Wong, Rui Wang
Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis
Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen