認知模型與人工智慧演算法為設計語言代理提供模板

摘要

儘管當代大型語言模型在獨立運作時表現日益出色，但仍有許多複雜問題超出單一模型的能力範圍。針對這類任務，學界對於如何將多個語言模型作為組件整合成更強大的系統仍存在不確定性。本立場文件主張，設計此類模組化語言代理的潛在藍圖，可從現有的認知模型與人工智慧演算法文獻中發掘。為闡明此觀點，我們將形式化「代理模板」的概念，明確定義單個語言模型的角色及其功能組合方式。接著系統性梳理文獻中各類現有語言代理，重點揭示其直接源自認知模型或AI演算法的底層模板。通過凸顯這些設計模式，我們旨在喚起學界對認知科學與AI啟發的代理模板之關注，將其視為開發高效且可解釋語言代理的重要工具。

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.

認知模型與人工智慧演算法為設計語言代理提供模板

Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents

摘要

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