多模态基础模型中的幻觉：成因、修正与评估综述
Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations
AI总结：
本综述系统梳理大型视觉-语言模型中的幻觉问题，分类其成因与缓解策略，评估现有基准的局限，并展望未来研究方向以提升模型可靠性。
摘要：
多模态基础模型代表了人工智能领域的重大飞跃。其中，大型视觉-语言模型（LVLMs）作为这些基础模型的典型代表，将视觉模态直接集成到大型语言模型（LLMs）中，展现出强大的信息处理与生成能力。然而，幻觉的存在限制了LVLMs在各个领域的潜力与实际效果。尽管已有大量工作致力于幻觉的缓解与修正，但系统性的综述却寥寥无几。为填补这一空白，本综述对LVLMs中的幻觉现象进行了系统梳理。我们从模型架构与数据质量两个维度对幻觉成因进行分类，并构建了现有缓解策略的全面分类体系。此外，我们从判别式与生成式两个视角严格评估了当前的幻觉评估基准，指出了现有指标的局限性。最后，本综述讨论了开放挑战与未来研究方向，以提升LVLMs的可靠性与可信度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
