# Liars' Bench: Evaluating Lie Detectors for Language Models

arXiv: https://arxiv.org/abs/2511.16035 · HTML: https://mlsanity.com/paper/2511.16035/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2025.

## Built on by (most cited first, 22 of 22)

- [Difficulties with Evaluating a Deception Detector for AIs](https://mlsanity.com/paper/2511.22662/) (2025)
- [The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes](https://mlsanity.com/paper/2602.15515/) (2026)
- [Linear representations in language models can change dramatically over a conversation](https://mlsanity.com/paper/2601.20834/) (2026)
- [One Probe Won't Catch Them All: Towards Targeted Deception Detection](https://mlsanity.com/paper/2602.01425/) (2026)
- [Linear Probe Accuracy Scales with Model Size and Benefits from Multi-Layer Ensembling](https://mlsanity.com/paper/2604.13386/) (2026)
- ["Did you lie?" Evaluating Lie Detectors across Model Scale and Belief-Verified Model Organisms](https://mlsanity.com/paper/2606.12618/) (2026)
- [Instrumental Choices: Measuring the Propensity of LLM Agents to Pursue Instrumental Behaviors](https://mlsanity.com/paper/2605.06490/) (2026)
- [Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing](https://mlsanity.com/paper/2606.17478/) (2026)
- [The Point of No Return: Counterfactual Localization of Deceptive Commitment in Language-Model Reasoning](https://mlsanity.com/paper/2605.17113/) (2026)
- [(Mis)generalization of Helpful-only Fine-tuning](https://mlsanity.com/paper/2606.04413/) (2026)
- [Scaling Trends for Lie Detector Oversight in Preference Learning](https://mlsanity.com/paper/2607.01567/) (2026)
- [Probing the Limits of the Lie Detector Approach to LLM Deception](https://mlsanity.com/paper/2603.10003/) (2026)
- [Do Linear Probes Generalize Better in Persona Coordinates?](https://mlsanity.com/paper/2605.09391/) (2026)
- [Confidently Deceptive: On the Relationship Between Confidence and Deception in LLMs](https://mlsanity.com/paper/2607.20444/) (2026)
- [Beyond Liars' Bench: The Impact of Lie Typology, Depth, and Sparsity on Deception Detection in LLMs](https://mlsanity.com/paper/2607.20479/) (2026)
- [Selecting The Most Informative Tokens in Natural Language Autoencoders](https://mlsanity.com/paper/2609.37040/) (2026)
- [Alignment via Training Against Probes Without Losing Monitorability](https://mlsanity.com/paper/2609.38645/) (2026)
- [Unlearning Deceptive Behaviors in LLMs with Contrastive Forget Sets](https://mlsanity.com/paper/2609.38909/) (2026)
- [Stress-Testing LLM Lie Detectors: Role-Play Failures and Spurious Correlations](https://mlsanity.com/paper/2609.39807/) (2026)
- [Caught in the Act: Probes Effectively Detect Sabotage and Catch Unverbalized Deception](https://mlsanity.com/paper/2610.12445/) (2026)
- [Accuracy and Uncertainty in Machine Deception Detection (Research White Paper)](https://mlsanity.com/paper/2610.15314/) (2026)
- [On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination](https://mlsanity.com/paper/2610.18727/) (2026)

## Abstract (arXiv)

Prior work has introduced techniques for detecting when large language models (LLMs) lie, that is, generate statements they believe are false. However, these techniques are typically validated in narrow settings that do not capture the diverse lies LLMs can generate. We introduce LIARS' BENCH, a testbed consisting of 72,863 examples of lies and honest responses generated by four open-weight models across seven datasets. Our settings capture qualitatively different types of lies and vary along two dimensions: the model's reason for lying and the object of belief targeted by the lie. Evaluating three black- and white-box lie detection techniques on LIARS' BENCH, we find that existing techniques systematically fail to identify certain types of lies, especially in settings where it's not possible to determine whether the model lied from the transcript alone. Overall, LIARS' BENCH reveals limitations in prior techniques and provides a practical testbed for guiding progress in lie detection.