HalluCounter: Reference-free LLM Hallucination Detection in the Wild!
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908381085696000 |
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| author | Urlana, Ashok Kanumolu, Gopichand Kumar, Charaka Vinayak Garlapati, Bala Mallikarjunarao Mishra, Rahul |
| author_facet | Urlana, Ashok Kanumolu, Gopichand Kumar, Charaka Vinayak Garlapati, Bala Mallikarjunarao Mishra, Rahul |
| contents | Response consistency-based, reference-free hallucination detection (RFHD) methods do not depend on internal model states, such as generation probabilities or gradients, which Grey-box models typically rely on but are inaccessible in closed-source LLMs. However, their inability to capture query-response alignment patterns often results in lower detection accuracy. Additionally, the lack of large-scale benchmark datasets spanning diverse domains remains a challenge, as most existing datasets are limited in size and scope. To this end, we propose HalluCounter, a novel reference-free hallucination detection method that utilizes both response-response and query-response consistency and alignment patterns. This enables the training of a classifier that detects hallucinations and provides a confidence score and an optimal response for user queries. Furthermore, we introduce HalluCounterEval, a benchmark dataset comprising both synthetically generated and human-curated samples across multiple domains. Our method outperforms state-of-the-art approaches by a significant margin, achieving over 90\% average confidence in hallucination detection across datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04615 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HalluCounter: Reference-free LLM Hallucination Detection in the Wild! Urlana, Ashok Kanumolu, Gopichand Kumar, Charaka Vinayak Garlapati, Bala Mallikarjunarao Mishra, Rahul Computation and Language Response consistency-based, reference-free hallucination detection (RFHD) methods do not depend on internal model states, such as generation probabilities or gradients, which Grey-box models typically rely on but are inaccessible in closed-source LLMs. However, their inability to capture query-response alignment patterns often results in lower detection accuracy. Additionally, the lack of large-scale benchmark datasets spanning diverse domains remains a challenge, as most existing datasets are limited in size and scope. To this end, we propose HalluCounter, a novel reference-free hallucination detection method that utilizes both response-response and query-response consistency and alignment patterns. This enables the training of a classifier that detects hallucinations and provides a confidence score and an optimal response for user queries. Furthermore, we introduce HalluCounterEval, a benchmark dataset comprising both synthetically generated and human-curated samples across multiple domains. Our method outperforms state-of-the-art approaches by a significant margin, achieving over 90\% average confidence in hallucination detection across datasets. |
| title | HalluCounter: Reference-free LLM Hallucination Detection in the Wild! |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.04615 |