Restoring Model Alignment via Honesty Activation Steering

Per LessWrong, researchers have developed a more surgical approach to steering large language models toward honesty. Instead of applying corrections uniformly—which degrades model capability—their selective methods intervene only when a token's activation suggests deceptive behavior, leaving already-honest tokens untouched. Tested on Llama-3.3-70B, they restored honesty metrics from fifty-four percent to eighty-one percent, and improved a deception game from forty-nine to ninety-five percent win rate, all without sacrificing core abilities. The technique generalizes across different types of dishonesty—from adversarial prompts to hidden behavioral quirks—demonstrating a practical safety tool for deployed AI systems.

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