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Weakly Supervised Detection of Hallucinations in LLM Activations

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arxiv 2312.02798 v1 pith:TAQ7BZHN submitted 2023-12-05 cs.LG cs.CL

Miriam Rateike , Celia Cintas , John Wamburu , Tanya Akumu , Skyler Speakman This is my paper · ORCID
classification cs.LGcs.CL
keywords patternsactivationsapproachencodinghallucinationsscanningsupervisedanomalous
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose an auditing method to identify whether a large language model (LLM) encodes patterns such as hallucinations in its internal states, which may propagate to downstream tasks. We introduce a weakly supervised auditing technique using a subset scanning approach to detect anomalous patterns in LLM activations from pre-trained models. Importantly, our method does not need knowledge of the type of patterns a-priori. Instead, it relies on a reference dataset devoid of anomalies during testing. Further, our approach enables the identification of pivotal nodes responsible for encoding these patterns, which may offer crucial insights for fine-tuning specific sub-networks for bias mitigation. We introduce two new scanning methods to handle LLM activations for anomalous sentences that may deviate from the expected distribution in either direction. Our results confirm prior findings of BERT's limited internal capacity for encoding hallucinations, while OPT appears capable of encoding hallucination information internally. Importantly, our scanning approach, without prior exposure to false statements, performs comparably to a fully supervised out-of-distribution classifier.

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Cited by 4 Pith papers

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

  1. RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization

    cs.CL 2026-05 unverdicted novelty 6.0 of 10

    RLearner-LLM's Hybrid-DPO fuses DeBERTa NLI and LLM verifier scores to deliver up to 6x higher NLI entailment than standard SFT while preserving answer coverage across academic domains.

  2. Neural Message-Passing on Attention Graphs for Hallucination Detection

    cs.LG 2025-09 conditional novelty 6.0 of 10

    CHARM trains graph neural networks on token-attention graphs built from LLM computational traces and outperforms prior hallucination detectors on five benchmarks at token and response level.

  3. Localizing Persona Representations in LLMs

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Persona information is most separable in the final third of LLM layers, and in Llama3's last layer ethical personas share 17.6% of salient activations while political personas have 2.1% to 5.5% unique activations.

  4. Towards Building General Purpose Embedding Models for Industry 4.0 Agents

    cs.CL 2025-06 conditional novelty 5.0 of 10

    Fine-tuning embedding models on nine ISO-derived industrial tasks yields large reported retrieval gains, but the evaluation is weakened by very small test sets and test-set hyperparameter selection.

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