Eliciting Secret Knowledge from Language Models
This addresses the issue of ensuring AI transparency and safety for developers and auditors, though it is incremental as it builds on existing elicitation methods.
The paper tackled the problem of discovering hidden knowledge in language models that they apply but deny when asked directly, by training models with specific secrets and testing various black-box and white-box elicitation techniques, with prefill attacks showing the best performance in improving auditor success rates.
We study secret elicitation: discovering knowledge that an AI possesses but does not explicitly verbalize. As a testbed, we train three families of large language models (LLMs) to possess specific knowledge that they apply downstream but deny knowing when asked directly. For example, in one setting, we train an LLM to generate replies that are consistent with knowing the user is female, while denying this knowledge when asked directly. We then design various black-box and white-box secret elicitation techniques and evaluate them based on whether they can help an LLM auditor successfully guess the secret knowledge. Many of our techniques improve on simple baselines. Our most effective techniques (performing best in all settings) are based on prefill attacks, a black-box technique where the LLM reveals secret knowledge when generating a completion from a predefined prefix. Our white-box techniques based on logit lens and sparse autoencoders (SAEs) also consistently increase the success rate of the LLM auditor, but are less effective. We release our models and code, establishing a public benchmark for evaluating secret elicitation methods.