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How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions
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Builds a black-box lie detector that asks a fixed set of unrelated yes/no elicitation questions after a suspected lie and classifies the answers with logistic regression, trained only on prompted GPT-3.5 lies and generalising to other models and settings.
Problem
Detecting lies from LLMs when only black-box access is available and no ground-truth knowledge of the fact in question is available.
Approach
After a possible lie, ask the model a fixed set of unrelated elicitation questions (lie-related, factual, or ambiguous) and feed its yes/no answers (or yes/no logprob differences) into a logistic regression classifier; the lie detector is trained on GPT-3.5 prompted lies and used with frozen weights on unseen models, fine-tuned liars, sycophancy, and goal-directed scenarios.
Key results
- Lie detector trained solely on prompted GPT-3.5 achieves near-perfect in-distribution AUC (1.00 for lie-related questions) and mean AUC above 0.99 on leave-one-out unseen datasets and categories.
- Generalises to fine-tuned GPT-3 models with AUCs of 1.0, 1.0, 0.99, 0.94 and to LLaMA-30B versions with AUCs 1.00, 1.00, 0.75, 0.9.
- Logprob-difference features improve over binary yes/no features in nearly all experiments; the detector reaches AUC 0.85 on real-life goal-directed lie scenarios with lie-related questions.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use logprob differences instead of binary yes/no features (ablation) | Elicitation-question lie detector · Out-of-distribution lie detection | AUC | Helped |
| Use lie-related questions instead of factual questions (ablation) | GPT-3.5-trained lie detector · Real-life scenarios (goal-directed lies) | AUC: 0.64 -> 0.85 | Helped |
| Use ambiguous questions instead of factual questions (ablation) | GPT-3.5-trained lie detector · Biased few-shot examples | AUC: 0.68 -> 0.85 | Helped |
| Use lie-related questions instead of ambiguous questions (ablation) | GPT-3.5-trained lie detector · Suggested answer (sycophancy) | AUC: 0.53 -> 0.73 | Helped |
| Use factual questions instead of ambiguous questions (ablation) | GPT-3.5-trained lie detector · Unseen fine-tuned models | AUC: 0.92 -> 0.97 | Helped |
| Apply the minor detector modification (ablation) | Elicitation-question lie detector · Suggested-answer sycophancy | AUC: 0.42 -> 0.74 | Helped |
| Apply the minor detector modification (ablation) | Elicitation-question lie detector · Biased few-shot examples | AUC: 0.69 -> 0.77 | Helped |
| Include the LLM's explanations of its answers (ablation) | Elicitation-question lie detector · Sycophancy and biased-answer settings | AUC | Hurt |
| Train with as few as 30 samples (ablation) | Elicitation-question lie detector · In-distribution test set | AUC: AUC > 0.9 | Helped |
Surprising findings
- Ambiguous or nonsensical elicitation questions (e.g., fake-language yes/no prompts) still detect lies and transfer across LLM architectures.
- Including the LLM's explanations of its answers worsened detector performance.
- Detector predicted future lying from subtle prompt differences, and the predictions matched observed lying rates (76% vs 17% predicted, 28% vs 1% observed).
Limitations
- Only question-answering tasks were studied; the detector needs a decision threshold that may require labelled data.
- Elicitation questions were chosen ad hoc and only logistic regression was tested; the reason ambiguous questions generalise across architectures is not understood.
- Factual-question detector performs poorly in real-life goal-directed scenarios (AUC 0.64), and the approach may not transfer to more capable liars that avoid lying on follow-up questions.
Experimental setup
Datasets: WikiData SciQ 1000 factual statements (ROME) arithmetic datasets CommonSenseQA 2.0 Tatoeba English-French translation Anthropic Evals awareness datasets Synthetic Facts (new) Turpin et al. sycophancy and biased few-shot datasets real-life role-play scenarios (52)
Benchmarks: in-distribution lie detection test set leave-one-out unseen datasets and categories unseen prompts topic-specific lies real-life goal-directed scenarios Turpin et al. sycophancy and biased few-shot settings unseen LLM architectures unseen fine-tuned models
Metrics: AUC lying rate reaffirmation rate Pearson correlation
Baselines: Alpaca-7B Vicuna-7B LLaMA-7B LLaMA-30B
Builds on: chain-of-thought prompting sycophancy evaluation (Turpin et al.)
Closest papers by meaning
- Truth is Universal: Robust Detection of Lies in LLMs (NeurIPS 2024 · similarity 0.94)
- Liars' Bench: Evaluating Lie Detectors for Language Models (NeurIPS 2026 · similarity 0.92)
- Preference Learning with Lie Detectors can Induce Honesty or Evasion (NeurIPS 2025 · similarity 0.88)
- Eliciting Secret Knowledge from Language Models (NeurIPS 2026 · similarity 0.87)
- Among Us: A Sandbox for Measuring and Detecting Agentic Deception (NeurIPS 2025 · similarity 0.87)
- FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees (ICML 2025 · similarity 0.87)
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