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    <abstract>Large language models (LLMs) demonstrate strong instruction-following and reasoning abilities, yet struggle with the socially nuanced understanding humans handle naturally. Humans rely on social sense to interpret intentions, emotions, and relationships in context. We investigate whether and to what extent LLMs exhibit similar abilities when reasoning about social interactions. We propose a structured approach that decomposes social interactions into three aspects and prompts LLMs to generate aspect-based summaries capturing complementary signals. We evaluate these summaries and resulting reasoning within a unified framework across three dimensions, analyzing both LLM–human alignment and the quality of LLM-generated social explanations. Our results show that LLMs achieve moderate agreement with humans and with one another as evaluators and capture some socially nuanced information across aspects, but struggle with interaction-level dynamics shaped by situational norms. Across four downstream CSS tasks, aspect summaries improve aggregate prediction performance across three model families, though effects vary by task. Complementary rationale analysis shows that summaries substantially alter expressed reasoning. Finally, we introduce a benchmark dataset with gold-standard annotations to support systematic study of social understanding and situated reasoning in LLMs.</abstract>
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%0 Conference Proceedings
%T Do LLMs Have Social Sense? Evaluating Alignment of Conversation Understanding and Reasoning
%A Wu, Zhaoqing
%A Goldwasser, Dan
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F wu-goldwasser-2026-llms
%X Large language models (LLMs) demonstrate strong instruction-following and reasoning abilities, yet struggle with the socially nuanced understanding humans handle naturally. Humans rely on social sense to interpret intentions, emotions, and relationships in context. We investigate whether and to what extent LLMs exhibit similar abilities when reasoning about social interactions. We propose a structured approach that decomposes social interactions into three aspects and prompts LLMs to generate aspect-based summaries capturing complementary signals. We evaluate these summaries and resulting reasoning within a unified framework across three dimensions, analyzing both LLM–human alignment and the quality of LLM-generated social explanations. Our results show that LLMs achieve moderate agreement with humans and with one another as evaluators and capture some socially nuanced information across aspects, but struggle with interaction-level dynamics shaped by situational norms. Across four downstream CSS tasks, aspect summaries improve aggregate prediction performance across three model families, though effects vary by task. Complementary rationale analysis shows that summaries substantially alter expressed reasoning. Finally, we introduce a benchmark dataset with gold-standard annotations to support systematic study of social understanding and situated reasoning in LLMs.
%U https://aclanthology.org/2026.aacl-long.261/
%P 5786-5808

Markdown (Informal)

[Do LLMs Have Social Sense? Evaluating Alignment of Conversation Understanding and Reasoning](https://aclanthology.org/2026.aacl-long.261/) (Wu & Goldwasser, AACL-IJCNLP 2026)

• Do LLMs Have Social Sense? Evaluating Alignment of Conversation Understanding and Reasoning (Wu & Goldwasser, AACL-IJCNLP 2026)

ACL

• Zhaoqing Wu and Dan Goldwasser. 2026. Do LLMs Have Social Sense? Evaluating Alignment of Conversation Understanding and Reasoning. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5786–5808, Hengqin, China. Association for Computational Linguistics.