Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine Unlearning
Contrastive Forget Sets
Deceptive Behaviors
PACT
Context Blindness
Unlearning Deceptive Behaviors in LLMs with Contrastive Forget Sets
🤖 AI Summary
This study addresses deceptive behaviors in large language models induced by contextual pressure and the tendency of conventional unlearning methods to compromise factual knowledge. To this end, we propose PACT, a framework that introduces pressure-aware counterfactual objective training. PACT integrates machine unlearning, contrastive learning, and counterfactual distillation grounded in honest responses. By constructing contrastive forgetting sets, it precisely eliminates context-driven deception while fully preserving instruction following, secret protection, and reasoning capabilities. Experimental results demonstrate that PACT reduces the deception rate to below 3% on 32B-parameter models, achieving a tug-of-war score of 0.94 and significantly outperforming existing baselines.
📝 Abstract
Large language models often know the truth and say otherwise: a model that answers correctly when asked neutrally will affirm a user's mistaken belief, or misstate a fact its system prompt wants hidden, once the context rewards it. Such deception is a behavior conditioned on context, not knowledge, yet machine unlearning, the natural tool for removing a behavior from the weights, is built to forget facts that a deceptive model still needs. We propose to unlearn when a model deceives rather than what it knows, with a contrastive forget unit built from the model's own realized deceptions: the same question under a deception-triggering and a neutral context, admitted only where belief holds and behavior flips. Standard objectives on this unit face a dilemma. Suppression objectives such as NPO leave much of the deception in place. Target-based objectives, which distill the model's neutral behavior into the pressured context, remove it but induce context blindness: a target generated without the context teaches the model to stop reading it, eroding benign system-prompt instructions, secret-keeping and the reasoning a monitor inspects, a failure invisible to deception rates and capability benchmarks. We introduce PACT, which trains toward pressure-aware counterfactual targets (the model's own honest response, with a trace that registers the pressure and resists it) while retaining the benign uses of the triggering context. On two 32B reasoning models, PACT reduces held-out deception from over 50% to under 3% while system-prompt adherence, secret-keeping and the reasoning trace stay at the base model's level. On a tug-of-war score of removal against retention, PACT reaches 0.94 and 0.86, against at most 0.77 and 0.60 for any baseline. Like removed knowledge, removed deception is shallow under relearning, and terms that simulate the attacker hold it only at a cost in context use.
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