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llm-safety

topic2 events
papersTODAY 04:00 UTC

K-Bench: clinician-calibrated benchmark for LLM safety in high-risk mental health chats

Researchers introduced K-Bench, a benchmark designed with clinician input to assess how large language models handle high-risk mental health conversations that escalate over time. The work addresses the limited understanding of LLM safety in these evolving support dialogues, where users increasingly turn for help. The benchmark provides a protected evaluation framework for measuring model performance in these sensitive settings.

safety/benchmark/mental-health/llm-evaluation
papersSEP 12 04:00 UTC

Paper compares diff-in-means and INLP for finding refusal directions in LLMs

A preprint revisits the finding that refusal behavior in safety-tuned chat models is controlled by a single linear direction in the residual stream, which can be recovered by taking the difference in means between harmful and harmless activations. The authors compare this diff-in-means approach with INLP, an iterative nullspace projection method, to see whether a one-direction account holds up. The work is presented as a preliminary comparison of the two techniques.

safety/interpretability/llm/alignment