How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions

• URL: http://arxiv.org/abs/2309.15840v1
• Date: Tue, 26 Sep 2023 16:07:54 GMT
• Title: How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions
• Authors: Lorenzo Pacchiardi, Alex J. Chan, S\"oren Mindermann, Ilan Moscovitz, Alexa Y. Pan, Yarin Gal, Owain Evans, Jan Brauner
• Abstract summary: Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense. Here, we develop a simple lie detector that requires neither access to the LLM's activations nor ground-truth knowledge of the fact in question. Despite its simplicity, this lie detector is highly accurate and surprisingly general.
• Score: 34.53980255211931
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense. LLMs might "lie", for example, when instructed to output misinformation. Here, we develop a simple lie detector that requires neither access to the LLM's activations (black-box) nor ground-truth knowledge of the fact in question. The detector works by asking a predefined set of unrelated follow-up questions after a suspected lie, and feeding the LLM's yes/no answers into a logistic regression classifier. Despite its simplicity, this lie detector is highly accurate and surprisingly general. When trained on examples from a single setting -- prompting GPT-3.5 to lie about factual questions -- the detector generalises out-of-distribution to (1) other LLM architectures, (2) LLMs fine-tuned to lie, (3) sycophantic lies, and (4) lies emerging in real-life scenarios such as sales. These results indicate that LLMs have distinctive lie-related behavioural patterns, consistent across architectures and contexts, which could enable general-purpose lie detection.

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