为什么LVLMs在更长回答中更容易产生幻觉：上下文的作用
Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context
AI总结：
本研究提出“诱导-检测-抑制”框架，证明LVLMs更长回答中的幻觉源于上下文依赖而非长度，并在所有基准上显著缓解幻觉。
摘要：
大型视觉-语言模型（LVLMs）近年来取得了显著进展，但也容易出现幻觉问题。它们在更长、更自由的回答中表现出更多的幻觉，这通常被归因于累积的不确定性。在本文中，我们提出疑问：幻觉的增加是否仅仅由长度引起的错误导致，还是存在更深层的潜在机制？经过一系列初步实验和发现，我们提出幻觉风险并非由长度本身引起，而是由于在更长回答中为了连贯性和完整性而对上下文的依赖增加所致。基于这些见解，我们提出了一种新颖的“诱导-检测-抑制”框架，该框架通过精心设计的上下文主动诱导幻觉，利用诱导实例对高风险案例进行早期检测，并最终在实际解码过程中抑制潜在的对象级幻觉。我们的方法在所有基准测试中均取得了一致且显著的改进，证明了其有效性。强大的检测能力和改进的幻觉缓解不仅验证了我们的框架，更重要的是，重新验证了我们关于上下文的假设。本研究不仅仅追求性能提升，更旨在提供新的见解，并作为深入探索LVLMs更长回答中幻觉问题的第一步。
英文摘要：
Large Vision-Language Models (LVLMs) have made significant progress in recent years but are also prone to hallucination issues. They exhibit more hallucinations in longer, free-form responses, often attributed to accumulated uncertainties. In this paper, we ask: Does increased hallucination result solely from length-induced errors, or is there a deeper underlying mechanism? After a series of preliminary experiments and findings, we suggest that the risk of hallucinations is not caused by length itself but by the increased reliance on context for coherence and completeness in longer responses. Building on these insights, we propose a novel "induce-detect-suppress" framework that actively induces hallucinations through deliberately designed contexts, leverages induced instances for early detection of high-risk cases, and ultimately suppresses potential object-level hallucinations during actual decoding. Our approach achieves consistent, significant improvements across all benchmarks, demonstrating its efficacy. The strong detection and improved hallucination mitigation not only validate our framework but, more importantly, re-validate our hypothesis on context. Rather than solely pursuing performance gains, this study aims to provide new insights and serves as a first step toward a deeper exploration of hallucinations in LVLMs' longer responses.
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。
