Numerous large language model (LLM) agents have been built for different tasks like web navigation and online shopping due to LLM's wide knowledge and text-understanding ability. Among these works, many of them utilize in-context examples to achieve generalization without the need for fine-tuning, while few of them have considered the problem of how to select and effectively utilize these examples. Recently, methods based on trajectory-level retrieval with task meta-data and using trajectories as in-context examples have been proposed to improve the agent's overall performance in some sequential decision making tasks. However, these methods can be problematic due to plausible examples retrieved without task-specific state transition dynamics and long input with plenty of irrelevant context. In this paper, we propose a novel framework (TRAD) to address these issues. TRAD first conducts Thought Retrieval, achieving step-level demonstration selection via thought matching, leading to more helpful demonstrations and less irrelevant input noise. Then, TRAD introduces Aligned Decision, complementing retrieved demonstration steps with their previous or subsequent steps, which enables tolerance for imperfect thought and provides a choice for balance between more context and less noise. Extensive experiments on ALFWorld and Mind2Web benchmarks show that TRAD not only outperforms state-of-the-art models but also effectively helps in reducing noise and promoting generalization. Furthermore, TRAD has been deployed in real-world scenarios of a global business insurance company and improves the success rate of robotic process automation.
Abstraction-of-Thought Makes Language Models Better Reasoners
Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能学院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tencent AI Lab（腾讯AI实验室）
TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought
Macao Polytechnic University（澳门理工大学）；Guangdong Institute of Intelligence Science and Technology（广东智能科学技术研究院）；Zhejiang Chinese Medical University（浙江中医药大学）；Hangzhou Ganzhicao Technology Co., Ltd（杭州甘之草科技有限公司）；BoardWare Information System Limited（BoardWare信息系统有限公司）
Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media
College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shenzhen X-Institute（深圳X研究院）；Academy of Cyber, Beijing（北京网络研究院）
Draw with Thought: Unleashing Multimodal Reasoning for Scientific Diagram Generation
Nanjing University of Information Science \& Technology Nanjing China；East China Normal University Shanghai China；The Hong Kong University of Science；Brown University Providence America；Southwest Jiaotong University Chengdu China；Nanjing University of Information Science \& Technology；East China Normal University；Brown University；Southwest Jiaotong University
提出无需训练的框架Draw with Thought，通过认知思维链推理将科学图表重建为可编辑的mxGraph XML代码，并发布Plot2XML基准，实验证明该方法能生成高保真且结构有效的重建结果。
ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models
School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；Research Center for SCIR, Harbin Institute of Technology（SCIR研究中心，哈尔滨工业大学）；NExT Research Center, National University of Singapore（NExT研究中心，新加坡国立大学）
Chain Of Thought Compression: A Theoretical Analysis
School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China（山西大学计算机与信息学院）；Queen Mary, University of London, UK（伦敦大学女王学院）；School of Informatics, University of Edinburgh, UK（爱丁堡大学信息学院）
