Artificial Agency and Large Language Models

TL;DR

This paper proposes a theoretical framework to define artificial agency, analyzes current large language models within this framework, and suggests pathways and challenges for developing truly autonomous artificial agents.

Contribution

It introduces a formal model of agency applicable to AI systems and discusses how current LLMs relate to this model, proposing future directions for creating artificial agents.

Findings

Current LLMs are not yet agents but show elements of agency.

A combined architecture could enable artificial agency.

Future research should address obstacles in building autonomous AI agents.

Abstract

The arrival of Large Language Models (LLMs) has stirred up philosophical debates about the possibility of realizing agency in an artificial manner. In this work we contribute to the debate by presenting a theoretical model that can be used as a threshold conception for artificial agents. The model defines agents as systems whose actions and goals are always influenced by a dynamic framework of factors that consists of the agent's accessible history, its adaptive repertoire and its external environment. This framework, in turn, is influenced by the actions that the agent takes and the goals that it forms. We show with the help of the model that state-of-the-art LLMs are not agents yet, but that there are elements to them that suggest a way forward. The paper argues that a combination of the agent architecture presented in Park et al. (2023) together with the use of modules like the Coscientist in Boiko et al. (2023) could potentially be a way to realize agency in an artificial manner. We end the paper by reflecting on the obstacles one might face in building such an artificial agent and by presenting possible directions for future research.