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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00900 |
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| _version_ | 1866908389373640704 |
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| author | Zhou, Jinfeng Chen, Yuxuan Shi, Yihan Zhang, Xuanming Lei, Leqi Feng, Yi Xiong, Zexuan Yan, Miao Wang, Xunzhi Cao, Yaru Yin, Jianing Wang, Shuai Dai, Quanyu Dong, Zhenhua Wang, Hongning Huang, Minlie |
| author_facet | Zhou, Jinfeng Chen, Yuxuan Shi, Yihan Zhang, Xuanming Lei, Leqi Feng, Yi Xiong, Zexuan Yan, Miao Wang, Xunzhi Cao, Yaru Yin, Jianing Wang, Shuai Dai, Quanyu Dong, Zhenhua Wang, Hongning Huang, Minlie |
| contents | LLMs exhibit promising Social Intelligence (SI) in modeling human behavior, raising the need to evaluate LLMs' SI and their discrepancy with humans. SI equips humans with interpersonal abilities to behave wisely in navigating social interactions to achieve social goals. This presents an operational evaluation paradigm: outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation, which existing work fails to address. To this end, we propose SocialEval, a script-based bilingual SI benchmark, integrating outcome- and process-oriented evaluation by manually crafting narrative scripts. Each script is structured as a world tree that contains plot lines driven by interpersonal ability, providing a comprehensive view of how LLMs navigate social interactions. Experiments show that LLMs fall behind humans on both SI evaluations, exhibit prosociality, and prefer more positive social behaviors, even if they lead to goal failure. Analysis of LLMs' formed representation space and neuronal activations reveals that LLMs have developed ability-specific functional partitions akin to the human brain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00900 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SocialEval: Evaluating Social Intelligence of Large Language Models Zhou, Jinfeng Chen, Yuxuan Shi, Yihan Zhang, Xuanming Lei, Leqi Feng, Yi Xiong, Zexuan Yan, Miao Wang, Xunzhi Cao, Yaru Yin, Jianing Wang, Shuai Dai, Quanyu Dong, Zhenhua Wang, Hongning Huang, Minlie Computation and Language LLMs exhibit promising Social Intelligence (SI) in modeling human behavior, raising the need to evaluate LLMs' SI and their discrepancy with humans. SI equips humans with interpersonal abilities to behave wisely in navigating social interactions to achieve social goals. This presents an operational evaluation paradigm: outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation, which existing work fails to address. To this end, we propose SocialEval, a script-based bilingual SI benchmark, integrating outcome- and process-oriented evaluation by manually crafting narrative scripts. Each script is structured as a world tree that contains plot lines driven by interpersonal ability, providing a comprehensive view of how LLMs navigate social interactions. Experiments show that LLMs fall behind humans on both SI evaluations, exhibit prosociality, and prefer more positive social behaviors, even if they lead to goal failure. Analysis of LLMs' formed representation space and neuronal activations reveals that LLMs have developed ability-specific functional partitions akin to the human brain. |
| title | SocialEval: Evaluating Social Intelligence of Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.00900 |