@inproceedings{espinosa-anke-perez-almendros-2026-analysing,
title = "Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study",
author = "Espinosa Anke, Luis and
Perez Almendros, Carla",
editor = "Mitkov, Ruslan and
Mu{\~n}oz, Rafael and
Lloret, Elena and
Ranasinghe, Tharindu and
Estevanell-Valladares, Ernesto L. and
Lamsiyah, Salima and
Montoyo, Andr{\'e}s and
Ezzini, Saad",
booktitle = "Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
month = jun,
year = "2026",
address = "Alicante, Spain",
publisher = "Department of Languages and Information Systems, University of Alicante",
url = "https://aclanthology.org/2026.nlpaics-1.16/",
pages = "156--162",
abstract = "Self-harm content is particularly challenging to detect using NLP techniques, and is also a high-stakes task which requires the highest accuracy to enable timely intervention or flagging at-risk users. We therefore present an analysis of how LLMs represent such self-harm content, which has downstream applications in self-harm detection, LLM intervention and governance and policing. In this paper, we focus on two datasets and four models, and perform two main experiments: (1) We train and evaluate linear probes across all layers of each model on two self-harm datasets: X-Sensitive and SH-Detection. Across both corpora, self-harm information crystallizes in the final 3 - 7{\%} of network layers (93 to 97{\%} depth). (2) We extract contrastive self-harm directions and, after performing a normaliation step, we find that the most accurate probes are not necessarily the most linearly separable. In particular, we find Gemma-3-4B to represent this \textit{contrastive self-harm direction} in a slightly different, more intricate way than the other LLMs."
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<abstract>Self-harm content is particularly challenging to detect using NLP techniques, and is also a high-stakes task which requires the highest accuracy to enable timely intervention or flagging at-risk users. We therefore present an analysis of how LLMs represent such self-harm content, which has downstream applications in self-harm detection, LLM intervention and governance and policing. In this paper, we focus on two datasets and four models, and perform two main experiments: (1) We train and evaluate linear probes across all layers of each model on two self-harm datasets: X-Sensitive and SH-Detection. Across both corpora, self-harm information crystallizes in the final 3 - 7% of network layers (93 to 97% depth). (2) We extract contrastive self-harm directions and, after performing a normaliation step, we find that the most accurate probes are not necessarily the most linearly separable. In particular, we find Gemma-3-4B to represent this contrastive self-harm direction in a slightly different, more intricate way than the other LLMs.</abstract>
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%0 Conference Proceedings %T Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study %A Espinosa Anke, Luis %A Perez Almendros, Carla %Y Mitkov, Ruslan %Y Muñoz, Rafael %Y Lloret, Elena %Y Ranasinghe, Tharindu %Y Estevanell-Valladares, Ernesto L. %Y Lamsiyah, Salima %Y Montoyo, Andrés %Y Ezzini, Saad %S Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security %D 2026 %8 June %I Department of Languages and Information Systems, University of Alicante %C Alicante, Spain %F espinosa-anke-perez-almendros-2026-analysing %X Self-harm content is particularly challenging to detect using NLP techniques, and is also a high-stakes task which requires the highest accuracy to enable timely intervention or flagging at-risk users. We therefore present an analysis of how LLMs represent such self-harm content, which has downstream applications in self-harm detection, LLM intervention and governance and policing. In this paper, we focus on two datasets and four models, and perform two main experiments: (1) We train and evaluate linear probes across all layers of each model on two self-harm datasets: X-Sensitive and SH-Detection. Across both corpora, self-harm information crystallizes in the final 3 - 7% of network layers (93 to 97% depth). (2) We extract contrastive self-harm directions and, after performing a normaliation step, we find that the most accurate probes are not necessarily the most linearly separable. In particular, we find Gemma-3-4B to represent this contrastive self-harm direction in a slightly different, more intricate way than the other LLMs. %U https://aclanthology.org/2026.nlpaics-1.16/ %P 156-162
Markdown (Informal)
[Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study](https://aclanthology.org/2026.nlpaics-1.16/) (Espinosa Anke & Perez Almendros, NLPAICS 2026)