认知模型与人工智能算法为设计语言智能体提供了模板。

摘要

尽管当代大型语言模型（LLM）在独立运行时已展现出日益强大的能力，但仍有诸多复杂问题超出了单个LLM的解决范围。针对此类任务，如何将多个LLM作为组件整合为更强大的系统仍存在不确定性。本立场文件提出，设计此类模块化语言智能体的潜在蓝图可借鉴现有认知模型与人工智能（AI）算法研究。为阐明这一观点，我们形式化地提出了智能体模板的概念——该模板既规定了单个LLM的角色定位，也明确了其功能组合方式。随后，我们系统梳理了文献中各类现有语言智能体，重点揭示了那些直接源于认知模型或AI算法的底层模板。通过凸显这些设计范式，我们旨在引导学界关注以认知科学和AI为启发的智能体模板，将其作为开发高效、可解释语言智能体的有力工具。

English

While contemporary large language models (LLMs) are increasingly capable in isolation, there are still many difficult problems that lie beyond the abilities of a single LLM. For such tasks, there is still uncertainty about how best to take many LLMs as parts and combine them into a greater whole. This position paper argues that potential blueprints for designing such modular language agents can be found in the existing literature on cognitive models and artificial intelligence (AI) algorithms. To make this point clear, we formalize the idea of an agent template that specifies roles for individual LLMs and how their functionalities should be composed. We then survey a variety of existing language agents in the literature and highlight their underlying templates derived directly from cognitive models or AI algorithms. By highlighting these designs, we aim to call attention to agent templates inspired by cognitive science and AI as a powerful tool for developing effective, interpretable language agents.

认知模型与人工智能算法为设计语言智能体提供了模板。

Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents

摘要

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