Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study

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.

Computation and Language (cs.CL)

arXiv:2607.21988 [cs.CL]

(or arXiv:2607.21988v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.21988 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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