AggTruth: Contextual Hallucination Detection using Aggregated Attention Scores in LLMs
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916806969524224 |
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| author | Matys, Piotr Eliasz, Jan Kiełczyński, Konrad Langner, Mikołaj Ferdinan, Teddy Kocoń, Jan Kazienko, Przemysław |
| author_facet | Matys, Piotr Eliasz, Jan Kiełczyński, Konrad Langner, Mikołaj Ferdinan, Teddy Kocoń, Jan Kazienko, Przemysław |
| contents | In real-world applications, Large Language Models (LLMs) often hallucinate, even in Retrieval-Augmented Generation (RAG) settings, which poses a significant challenge to their deployment. In this paper, we introduce AggTruth, a method for online detection of contextual hallucinations by analyzing the distribution of internal attention scores in the provided context (passage). Specifically, we propose four different variants of the method, each varying in the aggregation technique used to calculate attention scores. Across all LLMs examined, AggTruth demonstrated stable performance in both same-task and cross-task setups, outperforming the current SOTA in multiple scenarios. Furthermore, we conducted an in-depth analysis of feature selection techniques and examined how the number of selected attention heads impacts detection performance, demonstrating that careful selection of heads is essential to achieve optimal results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AggTruth: Contextual Hallucination Detection using Aggregated Attention Scores in LLMs Matys, Piotr Eliasz, Jan Kiełczyński, Konrad Langner, Mikołaj Ferdinan, Teddy Kocoń, Jan Kazienko, Przemysław Artificial Intelligence Computation and Language In real-world applications, Large Language Models (LLMs) often hallucinate, even in Retrieval-Augmented Generation (RAG) settings, which poses a significant challenge to their deployment. In this paper, we introduce AggTruth, a method for online detection of contextual hallucinations by analyzing the distribution of internal attention scores in the provided context (passage). Specifically, we propose four different variants of the method, each varying in the aggregation technique used to calculate attention scores. Across all LLMs examined, AggTruth demonstrated stable performance in both same-task and cross-task setups, outperforming the current SOTA in multiple scenarios. Furthermore, we conducted an in-depth analysis of feature selection techniques and examined how the number of selected attention heads impacts detection performance, demonstrating that careful selection of heads is essential to achieve optimal results. |
| title | AggTruth: Contextual Hallucination Detection using Aggregated Attention Scores in LLMs |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2506.18628 |