The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908489388916736 |
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| author | Janiak, Denis Binkowski, Jakub Sawczyn, Albert Gabrys, Bogdan Shwartz-Ziv, Ravid Kajdanowicz, Tomasz |
| author_facet | Janiak, Denis Binkowski, Jakub Sawczyn, Albert Gabrys, Bogdan Shwartz-Ziv, Ravid Kajdanowicz, Tomasz |
| contents | Large language models (LLMs) have revolutionized natural language processing, yet their tendency to hallucinate poses serious challenges for reliable deployment. Despite numerous hallucination detection methods, their evaluations often rely on ROUGE, a metric based on lexical overlap that misaligns with human judgments. Through comprehensive human studies, we demonstrate that while ROUGE exhibits high recall, its extremely low precision leads to misleading performance estimates. In fact, several established detection methods show performance drops of up to 45.9\% when assessed using human-aligned metrics like LLM-as-Judge. Moreover, our analysis reveals that simple heuristics based on response length can rival complex detection techniques, exposing a fundamental flaw in current evaluation practices. We argue that adopting semantically aware and robust evaluation frameworks is essential to accurately gauge the true performance of hallucination detection methods, ultimately ensuring the trustworthiness of LLM outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08285 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs Janiak, Denis Binkowski, Jakub Sawczyn, Albert Gabrys, Bogdan Shwartz-Ziv, Ravid Kajdanowicz, Tomasz Computation and Language Artificial Intelligence Machine Learning Large language models (LLMs) have revolutionized natural language processing, yet their tendency to hallucinate poses serious challenges for reliable deployment. Despite numerous hallucination detection methods, their evaluations often rely on ROUGE, a metric based on lexical overlap that misaligns with human judgments. Through comprehensive human studies, we demonstrate that while ROUGE exhibits high recall, its extremely low precision leads to misleading performance estimates. In fact, several established detection methods show performance drops of up to 45.9\% when assessed using human-aligned metrics like LLM-as-Judge. Moreover, our analysis reveals that simple heuristics based on response length can rival complex detection techniques, exposing a fundamental flaw in current evaluation practices. We argue that adopting semantically aware and robust evaluation frameworks is essential to accurately gauge the true performance of hallucination detection methods, ultimately ensuring the trustworthiness of LLM outputs. |
| title | The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.08285 |