@inproceedings{shi-etal-2026-danger,
title = "Danger Depends on the Mind: A Theory-of-Mind Grounded Dataset and Model for Context-Dependent Dangerous Speech",
author = "Shi, Yuanchen and
Zhang, Longyin and
Zhou, Guodong and
Kong, Fang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.322/",
pages = "6457--6478",
ISBN = "979-8-89176-395-1",
abstract = "Dangerous speech detection is a well-studied task, but existing approaches typically treat utterances in isolation, relying on binary labels that ignore who is speaking and in what mental state. We formulate a context-dependent variant of this task by grounding it in Theory-of-Mind (ToM). In cognitive science, ToM studies how humans attribute latent mental states-such as emotions, intentions, and actions-to others. We argue that such states are key signals for assessing the risk of an utterance. Building on this view, we construct ToM-DS, a 79K-instance dataset where each utterance is paired with structured speaker profiles, ToM states (emotion, intent, action), and topic hierarchies. During data construction, we first identify context-dependent sentences and generate diverse safe and dangerous scenarios surrounding them. High-quality annotations are obtained with state-of-the-art LLMs and a multi-stage cross-agent validation pipeline, yielding a comprehensive and reliable resource for context-dependent dangerous speech detection and fine-grained risk level classification. We further propose ToMGuard, a lightweight model with a dynamic ToM attention mechanism that adaptively weighs different mental-state cues. ToMGuard outperforms strong proprietary and open-source LLMs with significantly fewer parameters. Experimental results show that ToMGuard sets a new benchmark for context-dependent dangerous speech detection and risk level classification on ToM-DS."
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<abstract>Dangerous speech detection is a well-studied task, but existing approaches typically treat utterances in isolation, relying on binary labels that ignore who is speaking and in what mental state. We formulate a context-dependent variant of this task by grounding it in Theory-of-Mind (ToM). In cognitive science, ToM studies how humans attribute latent mental states-such as emotions, intentions, and actions-to others. We argue that such states are key signals for assessing the risk of an utterance. Building on this view, we construct ToM-DS, a 79K-instance dataset where each utterance is paired with structured speaker profiles, ToM states (emotion, intent, action), and topic hierarchies. During data construction, we first identify context-dependent sentences and generate diverse safe and dangerous scenarios surrounding them. High-quality annotations are obtained with state-of-the-art LLMs and a multi-stage cross-agent validation pipeline, yielding a comprehensive and reliable resource for context-dependent dangerous speech detection and fine-grained risk level classification. We further propose ToMGuard, a lightweight model with a dynamic ToM attention mechanism that adaptively weighs different mental-state cues. ToMGuard outperforms strong proprietary and open-source LLMs with significantly fewer parameters. Experimental results show that ToMGuard sets a new benchmark for context-dependent dangerous speech detection and risk level classification on ToM-DS.</abstract>
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%0 Conference Proceedings
%T Danger Depends on the Mind: A Theory-of-Mind Grounded Dataset and Model for Context-Dependent Dangerous Speech
%A Shi, Yuanchen
%A Zhang, Longyin
%A Zhou, Guodong
%A Kong, Fang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shi-etal-2026-danger
%X Dangerous speech detection is a well-studied task, but existing approaches typically treat utterances in isolation, relying on binary labels that ignore who is speaking and in what mental state. We formulate a context-dependent variant of this task by grounding it in Theory-of-Mind (ToM). In cognitive science, ToM studies how humans attribute latent mental states-such as emotions, intentions, and actions-to others. We argue that such states are key signals for assessing the risk of an utterance. Building on this view, we construct ToM-DS, a 79K-instance dataset where each utterance is paired with structured speaker profiles, ToM states (emotion, intent, action), and topic hierarchies. During data construction, we first identify context-dependent sentences and generate diverse safe and dangerous scenarios surrounding them. High-quality annotations are obtained with state-of-the-art LLMs and a multi-stage cross-agent validation pipeline, yielding a comprehensive and reliable resource for context-dependent dangerous speech detection and fine-grained risk level classification. We further propose ToMGuard, a lightweight model with a dynamic ToM attention mechanism that adaptively weighs different mental-state cues. ToMGuard outperforms strong proprietary and open-source LLMs with significantly fewer parameters. Experimental results show that ToMGuard sets a new benchmark for context-dependent dangerous speech detection and risk level classification on ToM-DS.
%U https://aclanthology.org/2026.findings-acl.322/
%P 6457-6478
Markdown (Informal)
[Danger Depends on the Mind: A Theory-of-Mind Grounded Dataset and Model for Context-Dependent Dangerous Speech](https://aclanthology.org/2026.findings-acl.322/) (Shi et al., Findings 2026)
ACL