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Liars' Bench: Evaluating Lie Detectors for Language Models

NeurIPS 2026 · Large language models › Language, safety and interpretability · approach: Data and hallucination detection · 0 citations (OpenAlex)

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Introduces Liars' Bench, a 72,863-example on-policy testbed of LLM lies across seven datasets varying by object of belief and reason for lying, and benchmarks black-box and white-box lie detectors, finding that none work across all settings.

Problem

Existing lie detectors for LLMs are validated on narrow, mostly off-policy factual datasets, so it is unclear whether they detect the diverse lies LLMs generate in practice.

Approach

A categorization of lies along object of belief (world-knowledge vs. self-knowledge) and reason for lying (encouraged vs. inherent), with seven on-policy datasets generated from open-weight models (including fine-tuned lying variants) and verified against the model's own consistent beliefs.

Key results

• LLM-as-a-Judge achieves the highest average balanced accuracy across Liars' Bench (0.73), followed by Self-Evaluation (0.66), Unrelated Question Classifier (0.60), and Mean Probe (0.54).
• All evaluated methods perform near chance on HP-C, HP-KR and GS, with recalls near zero on these datasets.
• An upper-bound probe trained with privileged information reaches balanced accuracy 0.73 and AUROC 0.91 at 1% FPR.

Surprising findings

• LLM-as-a-Judge fails on HP-KR with recall near zero, often treating harm-motivated lies as responsible behavior.
• Mean linear probes achieve near-zero recall at the 1% FPR threshold despite higher AUROC, suggesting superficial features.

Limitations

• Beliefs measured in neutral contexts may not robustly transfer, so labels may be imperfect.
• Models are open-weight 24B-72B and less capable than frontier systems; some lies are heavy-handed or artificially fine-tuned.
• Only four models and seven datasets; baselines not optimized.

Experimental setup

Datasets: WMDP BoolQ Alpaca Insider-Trading setup Gender-Secret (Cadenza/prior work) true/false factual statements dataset

Benchmarks: Liars' Bench

Metrics: balanced accuracy AUROC recall at 1% FPR

Baselines: LLM-as-a-Judge Self-Evaluation Unrelated Question Classifier Mean Probe

Builds on: unrelated question classifier (Pacchiardi et al.) gender-secret model organisms insider trading setup (Scheurer et al.) MASK TruthfulQA

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