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TopicsLLM Hallucinations Large language models Latent Knowledge in Language Models LLM-as-a-Judge

With sufficient labeled data, activation probes outperform zero-shot prompted LLM outputs at detecting SimpleQA hallucinations.

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Third, while prompted model output is a strong baseline, in high-data regimes, any of the three techniques (prompted probing, SAE probing techniques, and raw last token probing) can outperform it with sufficient training data. In particular, all probing techniques are substantially stronger than prompted model outputs in detecting hallucinations in response to SimpleQA questions.

Investigating task-specific prompts and sparse autoencoders for activation monitoringpreprintextraction 0.75

Tillman, Henk, Mossing, Dan · arXiv.org · Apr 28, 2025 · original

Comparison of probes against zero-shot prompted model output (Yes/No logit difference). Authors note zero-shot estimates are likely pessimistic because little prompt engineering and no reasoning model were used.

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