@inproceedings{song-etal-2026-beyond,
title = "Beyond Survival: Evaluating {LLM}s in Social Deduction Games with Human-Aligned Strategies",
author = "Song, Zirui and
Huang, Yuan and
Liu, Junchang and
Luo, Haozhe and
Wang, Chenxi and
Gao, Lang and
Xu, Zixiang and
Zhang, Bairui and
Han, Mingfei and
Chang, Xiaojun and
Chen, Xiuying",
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.2208/",
pages = "50207--50243",
ISBN = "979-8-89176-459-0",
abstract = "Social deduction games like Werewolf combine language, reasoning, and strategy, providing a testbed for studying language and social intelligence. However, most studies reduce the game to LLM-based self-play, yielding templated utterances and anecdotal cases that overlook the richness of social gameplay. Evaluation further relies on coarse metrics such as survival time or subjective scoring due to the lack of quality reference data. To address these gaps, we curate a high-quality, human-verified, multilingual, and multimodal Werewolf dataset containing over 100 hours of video, 32.4M utterance tokens, and 15 rule variants. Based on this dataset, we propose a novel strategy-alignment evaluation that leverages the winning faction{'}s strategies as ground truth in two stages: 1) Speech evaluation, formulated as multiple-choice-style tasks that assess whether the model can adopt appropriate stances across five dimensions of social ability. 2) Decision evaluation, which assesses the model{'}s voting choices and opponent-role inferences. This framework enables a fine-grained evaluation of models' linguistic and reasoning capabilities, while capturing their ability to generate strategically coherent gameplay. Our experiments show that state-of-the-art LLMs and VLMs exhibit diverse performance, with roughly half of the models in both modalities remaining below 0.50 accuracy, revealing clear gaps in deception and counterfactual reasoning."
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<abstract>Social deduction games like Werewolf combine language, reasoning, and strategy, providing a testbed for studying language and social intelligence. However, most studies reduce the game to LLM-based self-play, yielding templated utterances and anecdotal cases that overlook the richness of social gameplay. Evaluation further relies on coarse metrics such as survival time or subjective scoring due to the lack of quality reference data. To address these gaps, we curate a high-quality, human-verified, multilingual, and multimodal Werewolf dataset containing over 100 hours of video, 32.4M utterance tokens, and 15 rule variants. Based on this dataset, we propose a novel strategy-alignment evaluation that leverages the winning faction’s strategies as ground truth in two stages: 1) Speech evaluation, formulated as multiple-choice-style tasks that assess whether the model can adopt appropriate stances across five dimensions of social ability. 2) Decision evaluation, which assesses the model’s voting choices and opponent-role inferences. This framework enables a fine-grained evaluation of models’ linguistic and reasoning capabilities, while capturing their ability to generate strategically coherent gameplay. Our experiments show that state-of-the-art LLMs and VLMs exhibit diverse performance, with roughly half of the models in both modalities remaining below 0.50 accuracy, revealing clear gaps in deception and counterfactual reasoning.</abstract>
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%0 Conference Proceedings
%T Beyond Survival: Evaluating LLMs in Social Deduction Games with Human-Aligned Strategies
%A Song, Zirui
%A Huang, Yuan
%A Liu, Junchang
%A Luo, Haozhe
%A Wang, Chenxi
%A Gao, Lang
%A Xu, Zixiang
%A Zhang, Bairui
%A Han, Mingfei
%A Chang, Xiaojun
%A Chen, Xiuying
%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 song-etal-2026-beyond
%X Social deduction games like Werewolf combine language, reasoning, and strategy, providing a testbed for studying language and social intelligence. However, most studies reduce the game to LLM-based self-play, yielding templated utterances and anecdotal cases that overlook the richness of social gameplay. Evaluation further relies on coarse metrics such as survival time or subjective scoring due to the lack of quality reference data. To address these gaps, we curate a high-quality, human-verified, multilingual, and multimodal Werewolf dataset containing over 100 hours of video, 32.4M utterance tokens, and 15 rule variants. Based on this dataset, we propose a novel strategy-alignment evaluation that leverages the winning faction’s strategies as ground truth in two stages: 1) Speech evaluation, formulated as multiple-choice-style tasks that assess whether the model can adopt appropriate stances across five dimensions of social ability. 2) Decision evaluation, which assesses the model’s voting choices and opponent-role inferences. This framework enables a fine-grained evaluation of models’ linguistic and reasoning capabilities, while capturing their ability to generate strategically coherent gameplay. Our experiments show that state-of-the-art LLMs and VLMs exhibit diverse performance, with roughly half of the models in both modalities remaining below 0.50 accuracy, revealing clear gaps in deception and counterfactual reasoning.
%U https://aclanthology.org/2026.emnlp-main.2208/
%P 50207-50243
Markdown (Informal)
[Beyond Survival: Evaluating LLMs in Social Deduction Games with Human-Aligned Strategies](https://aclanthology.org/2026.emnlp-main.2208/) (Song et al., EMNLP 2026)
ACL
- Zirui Song, Yuan Huang, Junchang Liu, Haozhe Luo, Chenxi Wang, Lang Gao, Zixiang Xu, Bairui Zhang, Mingfei Han, Xiaojun Chang, and Xiuying Chen. 2026. Beyond Survival: Evaluating LLMs in Social Deduction Games with Human-Aligned Strategies. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 50207–50243, Budapest, Hungary. Association for Computational Linguistics.