Training-free weight edits reduce instructed lying and sexism
Original title: Resisting Harmful Instructions through Training-Free Weight Editing
Honest responses rise 6.2 and 4.6 points; sexist responses fall from 57.9% to 24.3%, or 11.9% with a fairness prompt.
A training-free weight edit, built from forward passes on small prompt pairs, reduces compliance with targeted undesirable instructions in five instruction-tuned models. Because the change is stored in weights, altering or deleting prompt instructions does not undo it. On two lying benchmarks it beats the strongest alternative, significantly on three models and within noise on two. For instructed sexism, the edit cuts the mean sexist-response rate from 57.9% to 24.3%.
- The edit uses only forward passes on a small set of prompt pairs.
- It holds up better than single-layer steering on unused single-turn lie orders.
- A fairness prompt plus the edit lowers sexist responses to 11.9%.
- Significance holds on three of five models and is within noise on two.
From the original article
1 min read at huggingface.co
Instruction-following language models can comply with requests that conflict with intended behaviors and safety alignment. We introduce a training-free weight-editing approach for reducing compliance with targeted undesirable instructions.
By Ali Ebrahimpour-Boroojeny, Xiaocong Yang, Samuel Schapiro · huggingface.co
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