这篇论文提出了检测模型生成描述时系统性偏差的新方法和基准SymbalBench，比之前方法好用4倍，适合需要审计多模态模型输出的人。
论文提出Symbal方法，利用现成基础模型通过双阶段结构检测多模态大模型生成图像描述时的系统性偏差。作者创建了SymbalBench基准，包含170万图像-文本对（自然图像和医学图像），组织成420个视觉语言数据集并标注了系统性偏差。Symbal在基准上正确识别63.8%数据集的偏差，比最强基线提升近4倍。实验还验证了Symbal能准确暴露4种多模态大模型生成描述中的错误，支持无需访问底层模型即可审计图像描述数据集。
Symbal: Detecting Systematic Misalignments in Model-Generated Captions
Multimodal large language models (MLLMs) often introduce errors when generating image captions, resulting in misaligned image-text pairs. Our work focuses on a class of captioning errors that we refer to as systematic misalignments, where a recurring error in MLLM-generated captions is closely associated with the presence of a specific visual feature in the paired image. Given a vision-language dataset with MLLM-generated captions, our aim in this work is to detect such errors, a task we refer to as systematic misalignment detection. As our first key contribution, we present Symbal, which utilizes a structured, dual-stage setup with off-the-shelf foundation models to identify systematic misalignments and summarize results in natural language. As our second key contribution, we introduce SymbalBench, a benchmark designed to evaluate automated methods on our proposed task. SymbalBench consists of 1.7 million image-text pairs from two domains (natural and medical images), organized into 420 vision-language datasets with annotated systematic misalignments. Symbal exhibits strong performance on this benchmark, correctly identifying systematic misalignments in 63.8% of datasets, a nearly 4x improvement over the closest baseline. We supplement our evaluations on SymbalBench with real-world evaluations, showing that (1) Symbal can accurately surface systematic misalignments in captions generated by four MLLMs and (2) Symbal is a powerful tool for auditing off-the-shelf image-caption datasets. Ultimately, our novel task, method, and benchmark can aid users with auditing MLLM-generated captions and identifying critical errors, without requiring access to the underlying MLLM. Code is available at https://github.com/Stanford-AIMI/Symbal.