Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents
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.
69a62d5b663a122a7ce66652
Ryan Liu
69a62d5b663a122a7ce66653
Dilip Arumugam
69a62d5b663a122a7ce66654
Cedegao E. Zhang
69a62d5b663a122a7ce66655
Sean Escola
69a62d5b663a122a7ce66656
Xaq Pitkow
69a62d5b663a122a7ce66657
Thomas L. Griffiths
Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents
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.
Large language models can be combined into modular agents using design principles from cognitive models and AI algorithms, with agent templates specifying roles and functional composition.
large language models modular language agents agent templates cognitive models artificial intelligence algorithms functional composition