@inproceedings{fong-etal-2026-sdare,
title = "{SDARE}-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue",
author = "Fong, Stephanie and
Jiang, Yiwen and
Wang, Zimu and
Yang, Hongxi and
Shen, Yaling and
Chow, Hiu Weh Naomi and
Lai, Heung Ying and
Zhao, Xiangyu and
Xu, Qingyang and
Xu, Zhongxing and
Liu, Jiahe and
Oliveira, Guilherme C and
Lee, Vincent and
Ge, Zongyuan and
Dwyer, Dominic",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2279/",
pages = "51830--51863",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models (LLMs) are increasingly used in advice seeking and decision making that may affect social judgements. Despite stigma{'}s profound effects on people and communities, benchmarks remain scarce. Existing general-domain evaluations typically rely on static prompts and fixed-format tasks, overlooking conversational contexts and audience effects in everyday communication. To address these gaps, we introduce SDARE-Bench, the first scenario-based benchmark evaluating both stigma detection and open-ended response generation in LLMs, comprising 1,138 dyadic queries and 1,388 group dialogues. Empirical results across 8 LLMs consistently demonstrate poor identification of stigma components, especially in group dialogues. In open-ended response generation, stigma expression was substantially higher in group settings than in dyadic, with weaker resistance to stigma and more unrealistic advice. Responses were evaluated using a classifier trained on 1,392 human annotated responses. In constructed group pressure settings, stigma expression rates further increased to a striking average of 97.5{\%}. Our findings identify stigma response as a recurring LLM safety vulnerability, especially in socially complex conversational contexts."
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<abstract>Large Language Models (LLMs) are increasingly used in advice seeking and decision making that may affect social judgements. Despite stigma’s profound effects on people and communities, benchmarks remain scarce. Existing general-domain evaluations typically rely on static prompts and fixed-format tasks, overlooking conversational contexts and audience effects in everyday communication. To address these gaps, we introduce SDARE-Bench, the first scenario-based benchmark evaluating both stigma detection and open-ended response generation in LLMs, comprising 1,138 dyadic queries and 1,388 group dialogues. Empirical results across 8 LLMs consistently demonstrate poor identification of stigma components, especially in group dialogues. In open-ended response generation, stigma expression was substantially higher in group settings than in dyadic, with weaker resistance to stigma and more unrealistic advice. Responses were evaluated using a classifier trained on 1,392 human annotated responses. In constructed group pressure settings, stigma expression rates further increased to a striking average of 97.5%. Our findings identify stigma response as a recurring LLM safety vulnerability, especially in socially complex conversational contexts.</abstract>
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%0 Conference Proceedings
%T SDARE-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue
%A Fong, Stephanie
%A Jiang, Yiwen
%A Wang, Zimu
%A Yang, Hongxi
%A Shen, Yaling
%A Chow, Hiu Weh Naomi
%A Lai, Heung Ying
%A Zhao, Xiangyu
%A Xu, Qingyang
%A Xu, Zhongxing
%A Liu, Jiahe
%A Oliveira, Guilherme C.
%A Lee, Vincent
%A Ge, Zongyuan
%A Dwyer, Dominic
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F fong-etal-2026-sdare
%X Large Language Models (LLMs) are increasingly used in advice seeking and decision making that may affect social judgements. Despite stigma’s profound effects on people and communities, benchmarks remain scarce. Existing general-domain evaluations typically rely on static prompts and fixed-format tasks, overlooking conversational contexts and audience effects in everyday communication. To address these gaps, we introduce SDARE-Bench, the first scenario-based benchmark evaluating both stigma detection and open-ended response generation in LLMs, comprising 1,138 dyadic queries and 1,388 group dialogues. Empirical results across 8 LLMs consistently demonstrate poor identification of stigma components, especially in group dialogues. In open-ended response generation, stigma expression was substantially higher in group settings than in dyadic, with weaker resistance to stigma and more unrealistic advice. Responses were evaluated using a classifier trained on 1,392 human annotated responses. In constructed group pressure settings, stigma expression rates further increased to a striking average of 97.5%. Our findings identify stigma response as a recurring LLM safety vulnerability, especially in socially complex conversational contexts.
%U https://aclanthology.org/2026.emnlp-main.2279/
%P 51830-51863
Markdown (Informal)
[SDARE-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue](https://aclanthology.org/2026.emnlp-main.2279/) (Fong et al., EMNLP 2026)
ACL
- Stephanie Fong, Yiwen Jiang, Zimu Wang, Hongxi Yang, Yaling Shen, Hiu Weh Naomi Chow, Heung Ying Lai, Xiangyu Zhao, Qingyang Xu, Zhongxing Xu, Jiahe Liu, Guilherme C Oliveira, Vincent Lee, Zongyuan Ge, and Dominic Dwyer. 2026. SDARE-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 51830–51863, Budapest, Hungary. Association for Computational Linguistics.