@inproceedings{zhang-etal-2025-sotopia,
title = "{SOTOPIA}-{\ensuremath{\Omega}}: Dynamic Strategy Injection Learning and Social Instruction Following Evaluation for Social Agents",
author = "Zhang, Wenyuan and
Liu, Tianyun and
Song, Mengxiao and
Li, Xiaodong and
Liu, Tingwen",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1203/",
doi = "10.18653/v1/2025.acl-long.1203",
pages = "24669--24697",
ISBN = "979-8-89176-251-0",
abstract = "Despite the abundance of prior social strategies possessed by humans, there remains a paucity of research dedicated to their transfer and integration into social agents. Our proposed SOTOPIA-{\ensuremath{\Omega}} framework aims to address and bridge this gap, with a particular focus on enhancing the social capabilities of language agents. This framework dynamically injects a variety of social strategies into expert agents, thereby automating the construction of 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 are complementary to social capability. We demonstrate that several 7B models trained on high-quality corpus not only significantly surpasses the expert agent (GPT-4) in achieving social goals but also enhances S-IF performance. Analysis and variant experiments validate the advantages of dynamic construction, which can especially break the agent{'}s prolonged deadlock."
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<abstract>Despite the abundance of prior social strategies possessed by humans, there remains a paucity of research dedicated to their transfer and integration into social agents. Our proposed SOTOPIA-\ensuremathØmega framework aims to address and bridge this gap, with a particular focus on enhancing the social capabilities of language agents. This framework dynamically injects a variety of social strategies into expert agents, thereby automating the construction of 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 are complementary to social capability. We demonstrate that several 7B models trained on high-quality corpus not only significantly surpasses the expert agent (GPT-4) in achieving social goals but also enhances S-IF performance. Analysis and variant experiments validate the advantages of dynamic construction, which can especially break the agent’s prolonged deadlock.</abstract>
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%0 Conference Proceedings
%T SOTOPIA-\ensuremathØmega: Dynamic Strategy Injection Learning and Social Instruction Following Evaluation for Social Agents
%A Zhang, Wenyuan
%A Liu, Tianyun
%A Song, Mengxiao
%A Li, Xiaodong
%A Liu, Tingwen
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhang-etal-2025-sotopia
%X Despite the abundance of prior social strategies possessed by humans, there remains a paucity of research dedicated to their transfer and integration into social agents. Our proposed SOTOPIA-\ensuremathØmega framework aims to address and bridge this gap, with a particular focus on enhancing the social capabilities of language agents. This framework dynamically injects a variety of social strategies into expert agents, thereby automating the construction of 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 are complementary to social capability. We demonstrate that several 7B models trained on high-quality corpus not only significantly surpasses the expert agent (GPT-4) in achieving social goals but also enhances S-IF performance. Analysis and variant experiments validate the advantages of dynamic construction, which can especially break the agent’s prolonged deadlock.
%R 10.18653/v1/2025.acl-long.1203
%U https://aclanthology.org/2025.acl-long.1203/
%U https://doi.org/10.18653/v1/2025.acl-long.1203
%P 24669-24697
Markdown (Informal)
[SOTOPIA-Ω: Dynamic Strategy Injection Learning and Social Instruction Following Evaluation for Social Agents](https://aclanthology.org/2025.acl-long.1203/) (Zhang et al., ACL 2025)
ACL