Unified Hallucination Detection for Multimodal Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929358849966080 |
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| author | Chen, Xiang Wang, Chenxi Xue, Yida Zhang, Ningyu Yang, Xiaoyan Li, Qiang Shen, Yue Liang, Lei Gu, Jinjie Chen, Huajun |
| author_facet | Chen, Xiang Wang, Chenxi Xue, Yida Zhang, Ningyu Yang, Xiaoyan Li, Qiang Shen, Yue Liang, Lei Gu, Jinjie Chen, Huajun |
| contents | Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detection of such hallucinations in MLLMs has, therefore, become a vital aspect of model evaluation and the safeguarding of practical application deployment. Prior research in this domain has been constrained by a narrow focus on singular tasks, an inadequate range of hallucination categories addressed, and a lack of detailed granularity. In response to these challenges, our work expands the investigative horizons of hallucination detection. We present a novel meta-evaluation benchmark, MHaluBench, meticulously crafted to facilitate the evaluation of advancements in hallucination detection methods. Additionally, we unveil a novel unified multimodal hallucination detection framework, UNIHD, which leverages a suite of auxiliary tools to validate the occurrence of hallucinations robustly. We demonstrate the effectiveness of UNIHD through meticulous evaluation and comprehensive analysis. We also provide strategic insights on the application of specific tools for addressing various categories of hallucinations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03190 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unified Hallucination Detection for Multimodal Large Language Models Chen, Xiang Wang, Chenxi Xue, Yida Zhang, Ningyu Yang, Xiaoyan Li, Qiang Shen, Yue Liang, Lei Gu, Jinjie Chen, Huajun Computation and Language Artificial Intelligence Information Retrieval Machine Learning Multimedia Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detection of such hallucinations in MLLMs has, therefore, become a vital aspect of model evaluation and the safeguarding of practical application deployment. Prior research in this domain has been constrained by a narrow focus on singular tasks, an inadequate range of hallucination categories addressed, and a lack of detailed granularity. In response to these challenges, our work expands the investigative horizons of hallucination detection. We present a novel meta-evaluation benchmark, MHaluBench, meticulously crafted to facilitate the evaluation of advancements in hallucination detection methods. Additionally, we unveil a novel unified multimodal hallucination detection framework, UNIHD, which leverages a suite of auxiliary tools to validate the occurrence of hallucinations robustly. We demonstrate the effectiveness of UNIHD through meticulous evaluation and comprehensive analysis. We also provide strategic insights on the application of specific tools for addressing various categories of hallucinations. |
| title | Unified Hallucination Detection for Multimodal Large Language Models |
| topic | Computation and Language Artificial Intelligence Information Retrieval Machine Learning Multimedia |
| url | https://arxiv.org/abs/2402.03190 |