Papers by Bogdan Gabrys
Hallucination Detection in LLMs Using Spectral Features of Attention Maps (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performance across tasks but remain prone to hallucinations. |
| Approach: | They propose a method that uses attention maps to detect hallucinations . they propose to use top-k eigenvalues of the attention maps as input to probes . |
| Outcome: | The proposed method achieves state-of-the-art hallucination detection performance among attention-based methods. |
FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs (2026.findings-eacl)
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| Challenge: | Existing methods to detect hallucinated content are limited by their tendency to generate factual errors. |
| Approach: | They propose a black-box sampling-based method that enables fine-grained fact-level detection by representing text as interpretable knowledge graphs consisting of facts in the form of triples. |
| Outcome: | The proposed method improves hallucination correction by 35.5% compared to baseline methods while sentence-level SelfCheckGPT yields only 10.6% improvement. |
The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs (2025.emnlp-main)
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Denis Janiak, Jakub Binkowski, Albert Sawczyn, Bogdan Gabrys, Ravid Shwartz-Ziv, Tomasz Jan Kajdanowicz
| Challenge: | Large language models (LLMs) have revolutionized natural language processing, but their tendency to hallucinate poses serious challenges for reliable deployment. |
| Approach: | They propose to use ROUGE to assess lexical overlap to determine accuracy of hallucination detection methods. |
| Outcome: | The proposed evaluation frameworks can rival complex methods, exposing a fundamental flaw in current evaluation practices. |