Computer Science > Computation and Language
arXiv:2509.15339 (cs)
[Submitted on 18 Sep 2025 (v1), last revised 9 Mar 2026 (this version, v2)]
Title:Quantifying Genuine Awareness in Hallucination Prediction Beyond Question-Side Shortcuts
View a PDF of the paper titled Quantifying Genuine Awareness in Hallucination Prediction Beyond Question-Side Shortcuts, by Yeongbin Seo and Dongha Lee and Jinyoung Yeo
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Abstract:Many works have proposed methodologies for language model (LM) hallucination detection and reported seemingly strong performance. However, we argue that the reported performance to date reflects not only a model's genuine awareness of its internal information, but also awareness derived purely from question-side information (e.g., benchmark hacking). While benchmark hacking can be effective for boosting hallucination detection score on existing benchmarks, it does not generalize to out-of-domain settings and practical usage. Nevertheless, disentangling how much of a model's hallucination detection performance arises from question-side awareness is non-trivial. To address this, we propose a methodology for measuring this effect without requiring human labor, Approximate Question-side Effect (AQE). Our analysis using AQE reveals that existing hallucination detection methods rely heavily on benchmark hacking.
| Subjects: | Computation and Language (cs.CL) |
| MSC classes: | 68T50 |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2509.15339 [cs.CL] |
| (or arXiv:2509.15339v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2509.15339
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arXiv-issued DOI via DataCite
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Submission history
From: Yeongbin Seo [view email][v1] Thu, 18 Sep 2025 18:29:14 UTC (1,294 KB)
[v2] Mon, 9 Mar 2026 23:55:31 UTC (1,311 KB)
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View a PDF of the paper titled Quantifying Genuine Awareness in Hallucination Prediction Beyond Question-Side Shortcuts, by Yeongbin Seo and Dongha Lee and Jinyoung Yeo
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