SOTOPIA-: Dynamic Strategy Injection Learning and Social Instruction Following Evaluation for Social Agents

Despite the abundance of prior social strategies humans possess, there remains a paucity of research dedicated to their transfer and integration into social agents. Our proposed SOTOPIA-Ω framework aims to address and bridge this gap, with a particular focus on enhancing the social capabilities of language agents. This framework dynamically injects multi-step reasoning strategies inspired by negotiation theory and two simple direct strategies into expert agents, thereby automating the construction of a high-quality social dialogue training corpus. Additionally, we introduce the concept of Social Instruction Following (S-IF) and propose two new S-IF evaluation metrics that complement social capability. We demonstrate that several 7B models trained on highquality corpus significantly surpass the expert agent (GPT-4) in achieving social goals and enhancing S-IF performance. Analysis and variant experiments validate the advantages of dynamic construction, which can especially break the agent's prolonged deadlock. * indicates corresponding author. 1 Zhou et al. (2024b) validates GPT-4's strong social capability and refers to it as an expert agent. Scenario: Two friends participating in a charity event for children in Syria. Task 1