Reasoning Path
Reasoning paths in large language models (LLMs) represent the sequence of steps a model takes to solve a problem, mirroring human thought processes. Current research focuses on improving the quality and efficiency of these paths through techniques like multi-agent systems, tree-based search algorithms (e.g., Tree of Thoughts), and methods that dynamically adjust the reasoning process based on task complexity and model confidence. This work is significant because enhanced reasoning paths lead to more accurate, reliable, and efficient LLM performance across diverse applications, from question answering and knowledge graph reasoning to complex problem-solving in robotics and other domains.
Papers
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models
Sungjae Lee, Hyejin Park, Jaechang Kim, Jungseul Ok
Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains
Vighnesh Subramaniam, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba, Shuang Li, Igor Mordatch
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs
Liyi Chen, Panrong Tong, Zhongming Jin, Ying Sun, Jieping Ye, Hui Xiong
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?
Zhanke Zhou, Rong Tao, Jianing Zhu, Yiwen Luo, Zengmao Wang, Bo Han