FREAK：一种用于高级MLLMs细粒度幻觉评估的基准测试
FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs
AI总结：
本文提出FREAK基准测试，通过高质量图像评估MLLMs在细粒度视觉感知中的幻觉问题，揭示了现有模型在详细视觉感知上的严重幻觉问题。
摘要：
多模态大语言模型（MLLMs）存在幻觉问题。现有幻觉评估基准往往受限于过于简化的任务导致指标饱和，或多样性不足无法充分评估最先进多模态模型的幻觉程度。为解决这一差距，我们提出了FREAK，一个全面的多模态基准，用于评估MLLMs的细粒度幻觉。通过高质量的逼真图像，包含细粒度反常识编辑，FREAK创新性地评估了MLLMs在详细视觉感知中的幻觉现象。在FREAK上的广泛实验显示，最先进模型在详细视觉感知方面存在严重的幻觉问题。为了进一步研究，我们整理了一个受控子集以间接评估模型感知目标详细信息的能力。通过系统评估该任务中的现有链式推理（CoT）提示技术，揭示了关于幻觉模式和模型推理过程的关键见解。
英文摘要：
Multimodal Large Language Models (MLLMs) suffer from hallucinations. Existing hallucination evaluation benchmarks are often limited by over-simplified tasks leading to saturated metrics, or insufficient diversity that fails to adequately assess the hallucination extent in state-of-the-art multimodal models. To address this gap, we propose FREAK, a comprehensive multimodal benchmark designed for fine-grained hallucination assessment in MLLMs. Through high-quality photorealistic images featuring fine-grained counter-commonsense edits, FREAK innovatively evaluates hallucination phenomena in detailed visual perception of MLLMs. Extensive experiments on FREAK show severe hallucination issues in SOTA models regarding detailed visual perception. To enable deeper investigation, we curate a controlled subset to indirectly evaluate the model's ability to perceive target detailed information. Through systematic evaluation of prevailing Chain-of-Thought (CoT) prompting techniques within this task, we reveal critical insights regarding hallucination patterns and model reasoning processes.
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。
