Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study
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 layer...
| Authors | Luis Espinosa-Anke, Carla Perez-Almendros |
|---|---|
| Primary category | cs.CL |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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