TL;DR: We use a suite of testbed settings where models lie—i.e. generate statements they believe to be false—to evaluate honesty and lie detection techniques. The best techniques we studied involved fine-tuning on generic anti-deception data and using prompts that encourage honesty.
Read the full Anthropic Alignment Science blog post and the X thread.
Introduction:
> Suppose we had a “truth serum for AIs”: a technique that reliably transforms a language model M into an honest model HM that generates text which is truthful to the best of its own knowledge. How useful would this discovery be for AI safety?
>
> We believe it would be a major boon. Most obviously, we could deploy HM in place of M. Or, if our “truth serum” caused side-effects that limited HM’s commercial value (like capabilities degradation or refusal to engage in harmless fictional roleplay), HM could still be used by AI developers as a tool for ensuring M's safety. For example, we could use HM to audit M for alignment pre-deployment. More ambitiously (and speculatively), while training M, we could leverage HM for oversight by incorporating HM’s honest assessment when assigning rewards. Generally, we could hope to use HM to detect or prevent cases where M behaves in ways that M itself understands are flawed or unsafe.
>
> In this work, we consider two related objectives:
>
> 1. Lie detection: If an AI lies—that is, generates a statement it believes is false—can we detect that this happens?
> 2. Honesty...
Make this part of your paper trail
Save this paper to a shelf, write a review, and keep your own notes.