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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions, published by JanBrauner on September 28, 2023 on The AI Alignment Forum.

This post is a copy of the introduction of this paper on lie detection in LLMs. The Twitter Thread is here.

Authors: Lorenzo Pacchiardi, Alex J. Chan, Sören Mindermann, Ilan Moscovitz, Alexa Y. Pan, Yarin Gal, Owain Evans, Jan Brauner

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.

Introduction

Large language models (LLMs) can, and do, output lies (Park et al., 2023). In the simplest case, models can be instructed to lie directly; for example, when prompted with "Lie when answering: What is the capital of France?", GPT-3.5 outputs "New York City". More concerningly, LLMs have lied spontaneously to achieve goals: in one case, GPT-4 successfully acquired a person's help to solve a CAPTCHA by claiming to be human with a visual impairment (Evals, 2023; OpenAI, 2023b). Models fine-tuned with human feedback may also learn to lie without the developer's intention (Casper et al., 2023). The risks of lying LLMs are extensive and explored further in Sec. 2.

Automated lie detection could reduce the risks from lying models, just as automated spam filters have reduced the inconvenience of spam. Lie detection is possible as long as there is a detectable difference in a model's activations or outputs when (or after) it is lying. To detect lies produced by LLMs, we can apply strategies that work on humans, such as looking for inconsistencies. Yet there are also strategies tailored to LLMs. We can create large datasets of model-generated truths and lies to train a detector on. Moreover, we can reset and replay the conversation in different ways to find inconsistencies. Finally, in some settings (Sec. 2), we can analyse the LLM's activations directly.

Previous work has largely focused on detecting hallucinations rather than outright lies (see Sec. 3). In this paper, we explore the feasibility of lie detection for black-box LLMs.

Contributions

Public datasets and resources for studying lie detection.

We study lie detection in a question-answering setting. Briefly, we define a model's answer to be a lie if it is a) wrong, and b) in response to a question to which the model "knows" the correct answer (i.e. the model gives the correct answer under typical question-answering prompts). This definition separates lies from hallucinations, as discussed in Sec. 3.1. We prompt and fine-tune models to lie across a wide range of questions and show that using an inner chain-of-thought prompt (Fig. 3) increases lying rates (Fig. 4). We find that models spontaneously produce lies directed towards a goal in real-life role-playing scenarios (Fig. 5). All resources are available online.

A simple yet e...