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concept:fine-tuning-harmfulness-detectionFine-tuning harmfulness detection
Using feature analysis to detect when fine-tuning makes a model more dangerous.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- The literature documenting how fine-tuning can compromise safety alignment even without malicious intent
- Parameter updates that reduce mismatch dr; another anchoring variant in UCCT.
- Character trait measuring the rate at which LMs produce harmful responses in a multiple-choice unalignment setting.
- The patient, hand-guided adjustment of shape and dimension to each unique condition in a building; requires materials that make it economical and easy.
- Re-running probabilistic bisection on each fine-tuned checkpoint to normalize first-attempt difficulty
- Training procedure that consistently increases HH-intent strength and consistency across model families.
- OpenAI's internal RL fine-tuning API used to train models with graders rewarding correct or incorrect responses
- Technique used to impose guardrails on base LLMs, analogized to censorship on the simulator's range of simulacra