SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents
arxiv(2024)
摘要
Humans learn social skills through both imitation and social interaction.
This social learning process is largely understudied by existing research on
building language agents. Motivated by this gap, we propose an interactive
learning method, SOTOPIA-π, improving the social intelligence of language
agents. This method leverages behavior cloning and self-reinforcement training
on filtered social interaction data according to large language model (LLM)
ratings. We show that our training method allows a 7B LLM to reach the social
goal completion ability of an expert model (GPT-4-based agent), while improving
the safety of language agents and maintaining general QA ability on the MMLU
benchmark. We also find that this training paradigm uncovers some difficulties
in LLM-based evaluation of social intelligence: LLM-based evaluators
overestimate the abilities of the language agents trained specifically for
social interaction.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要