点燃语言智能：从思维链推理到语言智能体的漫游指南
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
AI总结：
本综述全面梳理了思维链（CoT）推理技术的基础机制、范式转变及其在赋能自主语言智能体发展中的应用，并探讨了泛化、效率与安全等未来研究方向。
摘要：
大型语言模型（LLMs）显著增强了语言智能领域，这在它们于一系列复杂推理任务中展现的强大经验性能上得到了有力证明。此外，理论证明也阐明了其涌现的推理能力，为其在语言语境中的高级认知能力提供了极具说服力的展示。对于其在处理复杂推理任务中的卓越功效，LLMs利用了引人注目的思维链（CoT）推理技术，这要求它们在推导答案的过程中形成中间步骤。CoT推理方法不仅在提升推理性能方面表现出色，还在增强可解释性、可控性和灵活性方面表现突出。鉴于这些优势，近期的研究工作已将CoT推理方法扩展，以促进自主语言智能体的发展，这些智能体能够熟练遵循语言指令并在各种环境中执行动作。本综述论文组织了一场深入的探讨，剖析了重要的研究维度，包括：(i) CoT技术的基础机制，重点阐明其有效性背后的情形与依据；(ii) CoT的范式转变；(iii) 由CoT方法赋能的语言智能体的蓬勃发展。未来的研究途径涵盖对泛化、效率、定制化、扩展和安全性的探索。本文面向广泛的读者群体，包括寻求CoT推理和语言智能体全面知识的初学者，以及对基础机制感兴趣并希望参与这些主题前沿讨论的资深研究人员。相关论文的仓库可在 https://github.com/Zoeyyao27/CoT-Igniting-Agent 获取。
英文摘要：
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks. Additionally, theoretical proofs have illuminated their emergent reasoning capabilities, providing a compelling showcase of their advanced cognitive abilities in linguistic contexts. Critical to their remarkable efficacy in handling complex reasoning tasks, LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer. The CoT reasoning approach has not only exhibited proficiency in amplifying reasoning performance but also in enhancing interpretability, controllability, and flexibility. In light of these merits, recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents, which adeptly adhere to language instructions and execute actions within varied environments. This survey paper orchestrates a thorough discourse, penetrating vital research dimensions, encompassing: (i) the foundational mechanics of CoT techniques, with a focus on elucidating the circumstances and justification behind its efficacy; (ii) the paradigm shift in CoT; and (iii) the burgeoning of language agents fortified by CoT approaches. Prospective research avenues envelop explorations into generalization, efficiency, customization, scaling, and safety. This paper caters to a wide audience, including beginners seeking comprehensive knowledge of CoT reasoning and language agents, as well as experienced researchers interested in foundational mechanics and engaging in cutting-edge discussions on these topics. A repository for the related papers is available at https://github.com/Zoeyyao27/CoT-Igniting-Agent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
