从认知心理学视角调查VLM幻觉：迈向解释的第一步及有趣观察
Investigating VLM Hallucination from a Cognitive Psychology Perspective: A First Step Toward Interpretation with Intriguing Observations
AI总结：
从认知心理学视角提出导致VLM幻觉的认知偏差分类法，设计AIpsych基准揭示模型心理倾向，发现模型规模增大时谄媚倾向增强而权威偏差降低，为理解幻觉提供新视角。
摘要：
幻觉是视觉语言模型（VLMs）中长期存在的问题，一直被积极研究。现有研究通常将幻觉归因于技术限制或谄媚偏差，后者指模型倾向于生成错误答案以迎合用户期望。然而，这些解释主要关注技术或外部驱动因素，可能忽视了幻觉行为可能反映人类心理学中观察到的认知偏差的可能性。在这项工作中，我们引入了一个心理学分类法，对导致幻觉的VLMs认知偏差进行分类，包括谄媚、逻辑不一致，以及一种新识别的VLMs行为：诉诸权威。为了系统地分析这些行为，我们设计了AIpsych，这是一个可扩展的基准，揭示了模型响应模式中的心理倾向。利用该基准，我们调查了模型架构和参数规模的变化如何影响模型在响应策略性操纵问题时的行为。我们的实验表明，随着模型规模增加，VLMs表现出更强的谄媚倾向但权威偏差降低，这表明能力增强但响应完整性可能受损。一项人类受试者研究进一步验证了我们的假设，并突出了VLMs与人类受访者之间的关键行为差异。这项工作为理解VLMs中的幻觉提供了新视角，并强调了将心理学原理整合到模型评估中的重要性。
英文摘要：
Hallucination is a long-standing problem that has been actively investigated in Vision-Language Models (VLMs). Existing research commonly attributes hallucinations to technical limitations or sycophancy bias, where the latter means the models tend to generate incorrect answers to align with user expectations. However, these explanations primarily focus on technical or externally driven factors, and may have neglected the possibility that hallucination behaviours might mirror cognitive biases observed in human psychology. In this work, we introduce a psychological taxonomy, categorizing VLMs' cognitive biases that lead to hallucinations, including sycophancy, logical inconsistency, and a newly identified VLMs behaviour: appeal to authority. To systematically analyze these behaviours, we design AIpsych, a scalable benchmark that reveals psychological tendencies in model response patterns. Leveraging this benchmark, we investigate how variations in model architecture and parameter size influence model behaviour when responding to strategically manipulated questions. Our experiments reveal that as model size increases, VLMs exhibit stronger sycophantic tendencies but reduced authority bias, suggesting increasing competence but a potential erosion of response integrity. A human subject study further validates our hypotheses and highlights key behavioural differences between VLMs and human respondents. This work suggests a new perspective for understanding hallucination in VLMs and highlights the importance of integrating psychological principles into model evaluation.
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