Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distribution
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908555886460928 |
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| author | Dubanowska, Zuzanna Żelaszczyk, Maciej Brzozowski, Michał Mandica, Paolo Karpowicz, Michał |
| author_facet | Dubanowska, Zuzanna Żelaszczyk, Maciej Brzozowski, Michał Mandica, Paolo Karpowicz, Michał |
| contents | We critically assess the efficacy of the current SOTA in hallucination detection and find that its performance on the RAGTruth dataset is largely driven by a spurious correlation with data. Controlling for this effect, state-of-the-art performs no better than supervised linear probes, while requiring extensive hyperparameter tuning across datasets. Out-of-distribution generalization is currently out of reach, with all of the analyzed methods performing close to random. We propose a set of guidelines for hallucination detection and its evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19372 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distribution Dubanowska, Zuzanna Żelaszczyk, Maciej Brzozowski, Michał Mandica, Paolo Karpowicz, Michał Machine Learning Artificial Intelligence We critically assess the efficacy of the current SOTA in hallucination detection and find that its performance on the RAGTruth dataset is largely driven by a spurious correlation with data. Controlling for this effect, state-of-the-art performs no better than supervised linear probes, while requiring extensive hyperparameter tuning across datasets. Out-of-distribution generalization is currently out of reach, with all of the analyzed methods performing close to random. We propose a set of guidelines for hallucination detection and its evaluation. |
| title | Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distribution |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.19372 |