ART: Automatic multi-step reasoning and tool-use for large language models
University of Washington（华盛顿大学）；Microsoft Research（微软研究院）；University of California, Irvine（加州大学欧文分校）；Allen Institute of Artificial Intelligence（艾伦人工智能研究所）；Meta AI
Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, Marco Tulio Ribeiro
Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings by generating intermediate chain of thought (CoT) reasoning steps. Further, each reasoning step can rely on external tools to support computation beyond the core LLM capabilities (e.g. search/running code). Prior work on CoT prompting and tool use typically requires hand-crafting task-specific demonstrations and carefully scripted interleaving of model generations with tool use. We introduce Automatic Reasoning and Tool-use (ART), a framework that uses frozen LLMs to automatically generate intermediate reasoning steps as a program. Given a new task to solve, ART selects demonstrations of multi-step reasoning and tool use from a task library. At test time, ART seamlessly pauses generation whenever external tools are called, and integrates their output before resuming generation. ART achieves a substantial improvement over few-shot prompting and automatic CoT on unseen tasks in the BigBench and MMLU benchmarks, and matches performance of hand-crafted CoT prompts on a majority of these tasks. ART is also extensible, and makes it easy for humans to improve performance by correcting errors in task-specific programs or incorporating new tools, which we demonstrate by drastically improving performance on select tasks with minimal human intervention.
Stop Rewarding Hallucinated Steps: Faithfulness-Aware Step-Level Reinforcement Learning for Small Reasoning Models
Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳研究院）；Institute of Artificial Intelligence (TeleAI), China Telecom Corp Ltd（中国电信人工智能研究院）；College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China（浙江大学集成电路学院）；Zhongguancun Academy, Beijing, China（中关村学院）
Stepwise Reasoning Enhancement for LLMs via External Subgraph Generation
School of Information Science and Engineering, Chongqing Jiaotong University（重庆交通大学信息科学与工程学院）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）
AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Research Center for SCIR, Harbin Institute of Technology（哈尔滨工业大学SCIR研究中心）
A Stepwise-Enhanced Reasoning Framework for Large Language Models Based on External Subgraph Generation
School of Information Science and Engineering, Chongqing Jiaotong University（重庆交通大学信息科学与工程学院）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）
SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation
School of Information Science and Engineering, Chongqing Jiaotong University（重庆交通大学信息科学与工程学院）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）
Improving Reasoning Capabilities in Small Models through Mixture-of-Layers Distillation with Stepwise Attention on Key Information
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
The University of New South Wales（新南威尔士大学）；University of Göttingen（哥廷根大学）；Macquarie University（麦考瑞大学）；Beijing Intelligent Decision Medical Technology Co. Ltd（北京智决医疗科技有限公司）
Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning Distillation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
The Impact of Reasoning Step Length on Large Language Models
Rutgers University（罗格斯大学）；University of Liverpool（利物浦大学）；Northwestern University（西北大学）；New Jersey Institute of Technology（新泽西理工学院）；University of Exeter（埃克塞特大学）
