SINdex: Semantic INconsistency Index for Hallucination Detection in LLMs
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909531657732096 |
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| author | Abdaljalil, Samir Kurban, Hasan Sharma, Parichit Serpedin, Erchin Atat, Rachad |
| author_facet | Abdaljalil, Samir Kurban, Hasan Sharma, Parichit Serpedin, Erchin Atat, Rachad |
| contents | Large language models (LLMs) are increasingly deployed across diverse domains, yet they are prone to generating factually incorrect outputs - commonly known as "hallucinations." Among existing mitigation strategies, uncertainty-based methods are particularly attractive due to their ease of implementation, independence from external data, and compatibility with standard LLMs. In this work, we introduce a novel and scalable uncertainty-based semantic clustering framework for automated hallucination detection. Our approach leverages sentence embeddings and hierarchical clustering alongside a newly proposed inconsistency measure, SINdex, to yield more homogeneous clusters and more accurate detection of hallucination phenomena across various LLMs. Evaluations on prominent open- and closed-book QA datasets demonstrate that our method achieves AUROC improvements of up to 9.3% over state-of-the-art techniques. Extensive ablation studies further validate the effectiveness of each component in our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05980 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SINdex: Semantic INconsistency Index for Hallucination Detection in LLMs Abdaljalil, Samir Kurban, Hasan Sharma, Parichit Serpedin, Erchin Atat, Rachad Computation and Language Artificial Intelligence Large language models (LLMs) are increasingly deployed across diverse domains, yet they are prone to generating factually incorrect outputs - commonly known as "hallucinations." Among existing mitigation strategies, uncertainty-based methods are particularly attractive due to their ease of implementation, independence from external data, and compatibility with standard LLMs. In this work, we introduce a novel and scalable uncertainty-based semantic clustering framework for automated hallucination detection. Our approach leverages sentence embeddings and hierarchical clustering alongside a newly proposed inconsistency measure, SINdex, to yield more homogeneous clusters and more accurate detection of hallucination phenomena across various LLMs. Evaluations on prominent open- and closed-book QA datasets demonstrate that our method achieves AUROC improvements of up to 9.3% over state-of-the-art techniques. Extensive ablation studies further validate the effectiveness of each component in our framework. |
| title | SINdex: Semantic INconsistency Index for Hallucination Detection in LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.05980 |