多模态基础模型中的幻觉：成因、纠正与评估综述

Computer Science > Artificial Intelligence arXiv:2410.15359 (cs) [Submitted on 20 Oct 2024 (v1), last revised 13 Sep 2026 (this version, v2)] Title:Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations Authors:Yinghao Guo, Wei Lan, Wenyi Chen, Qingfeng Chen, Shichao Zhang, Shirui Pan, Huiyu Zhou, Yi Pan View a PDF of the paper titled Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations, by Yinghao Guo and 7 other authors View PDF HTML (experimental) Abstract:Multimodal Foundation Models represent a significant leap in artificial intelligence. Among them, Large Vision-Language Models (LVLMs) serve as the typical representative of these foundation models, which integrate visual modality directly into Large Language Models (LLMs). They have demonstrated strong capabilities in information processing and generation. However, the existence of hallucinations has limited the potential and practical effectiveness of LVLM in various fields. Although lots of work has been devoted to hallucination mitigation and correction, there are few reviews to summarize them. To address this gap, this survey provides a systematic review of the hallucination landscape in LVLMs. We categorize the causes related to model architecture and data quality, and construct a comprehensive taxonomy of existing mitigation strategies. Furthermore, we critically assess current hallucination evaluation benchmarks from both discriminative and generative perspectives, highlighting the limitations of existing metrics. This survey concludes by discussing open challenges and future research directions to advance the reliability and trustworthiness of LVLMs. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2410.15359 [cs.AI] (or arXiv:2410.15359v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2410.15359 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wenyi Chen [view email] [v1] Sun, 20 Oct 2024 10:58:58 UTC (685 KB) [v2] Sun, 13 Sep 2026 05:37:31 UTC (19,723 KB) Full-text links: Access Paper: View a PDF of the paper titled Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations, by Yinghao Guo and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2024-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)