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LLM Internal States Reveal Hallucination Risk Faced With a Query

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arxiv 2407.03282 v2 pith:OFN3IQNG submitted 2024-07-03 cs.CL

Ziwei Ji , Delong Chen , Etsuko Ishii , Samuel Cahyawijaya , Yejin Bang , Bryan Wilie , Pascale Fung This is my paper · ORCID
classification cs.CL
keywords hallucinationinternalllmsqueryriskstatesdatafaced
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The hallucination problem of Large Language Models (LLMs) significantly limits their reliability and trustworthiness. Humans have a self-awareness process that allows us to recognize what we don't know when faced with queries. Inspired by this, our paper investigates whether LLMs can estimate their own hallucination risk before response generation. We analyze the internal mechanisms of LLMs broadly both in terms of training data sources and across 15 diverse Natural Language Generation (NLG) tasks, spanning over 700 datasets. Our empirical analysis reveals two key insights: (1) LLM internal states indicate whether they have seen the query in training data or not; and (2) LLM internal states show they are likely to hallucinate or not regarding the query. Our study explores particular neurons, activation layers, and tokens that play a crucial role in the LLM perception of uncertainty and hallucination risk. By a probing estimator, we leverage LLM self-assessment, achieving an average hallucination estimation accuracy of 84.32\% at run time.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  3. Emergent Response Planning in LLMs

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Hidden representations of LLM prompts encode global attributes of the upcoming response, and simple probes can predict length, content choices, and answer confidence before generation begins.

  4. ISACL: Internal State Analyzer for Copyrighted Training Data Leakage

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    An MLP trained on LLM internal states predicts Rouge-L-defined literal copying leakage with high accuracy, but not paraphrase-level leakage.

  5. Cleanse: Uncertainty Estimation Approach Using Clustering-based Semantic Consistency in LLMs

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Cleanse detects hallucinated LLM answers by computing the share of hidden-embedding cosine similarity that falls inside semantic clusters, and it beats several baselines in AUROC across four models and two QA benchmarks.

  6. Factual Self-Awareness in Language Models: Representation, Robustness, and Scaling

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Language models encode a linear, decodable signal in their residual stream that predicts whether an upcoming factual recall will be correct.

  7. Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor

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    A black-box hallucination watchdog that stores previously hallucinated queries in a vector database and flags new queries by embedding similarity and semantic entropy.

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  9. Towards Harmonized Uncertainty Estimation for Large Language Models

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  10. Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models

    cs.CL 2025-06 reject novelty 3.0 of 10

    A survey of LLM hallucination research that formalizes hallucination types and argues, via incompleteness and undecidability arguments, that hallucinations cannot be fully eliminated.

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