Summary of Unified Hallucination Detection For Multimodal Large Language Models, by Xiang Chen and Chenxi Wang and Yida Xue and Ningyu Zhang and Xiaoyan Yang and Qiang Li and Yue Shen and Lei Liang and Jinjie Gu and Huajun Chen
Unified Hallucination Detection for Multimodal Large Language Models
by Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG); Multimedia (cs.MM)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the critical issue of hallucination in Multimodal Large Language Models (MLLMs), a significant challenge that hinders practical applications. The authors introduce MHaluBench, a novel meta-evaluation benchmark to evaluate advancements in hallucination detection methods. Additionally, they present UNIHD, a unified multimodal hallucination detection framework that leverages auxiliary tools to validate hallucinations robustly. The effectiveness of UNIHD is demonstrated through meticulous evaluation and comprehensive analysis. Furthermore, the authors provide strategic insights on applying specific tools for addressing various categories of hallucinations. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well artificial intelligence models can tell what’s real or not. Sometimes these models can get confused and make things up that aren’t actually there. The researchers created a special test to check if this is happening, called MHaluBench. They also made a new way to detect when this happens, called UNIHD. This helps us trust the models more and use them correctly. |
Keywords
* Artificial intelligence * Hallucination