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Source note3 min read

Symbal: Detecting Systematic Misalignments in Model-Generated Captions

Source

arXiv

arxiv.org/abs/2607.15216v1

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Plain-Language Brief

Researchers have developed Symbal, a method for detecting systematic misalignments in image-text pairs generated by multimodal large language models (MLLMs). These misalignments occur when the model consistently generates errors associated with specific visual features in images. The study presents a benchmark, SymbalBench, which includes 1.7 million image-text pairs and demonstrates that Symbal correctly identifies systematic misalignments in 63.8% of cases, a significant improvement over existing methods.

What the Document Says

  • The study focuses on systematic misalignments in MLLM-generated captions.
  • Symbal is presented as a method for detecting these misalignments using a structured, dual-stage setup with off-the-shelf foundation models.
  • SymbalBench is introduced as a benchmark for evaluating automated methods on systematic misalignment detection.
  • SymbalBench contains 1.7 million image-text pairs from two domains and 420 vision-language datasets with annotated systematic misalignments.
  • The study shows that Symbal performs well on SymbalBench, identifying systematic misalignments in 63.8% of cases, a significant improvement over the closest baseline.
  • Evaluations are also conducted in real-world scenarios, demonstrating that Symbal can accurately surface systematic misalignments in captions generated by four MLLMs and is useful for auditing off-the-shelf image-caption datasets.

Key Concepts Mentioned

  • multimodal large language models (MLLMs)
  • systematic misalignments
  • image-text pairs
  • Symbal
  • SymbalBench

Claims Made by the Document

  • Systematic misalignments exist in MLLM-generated captions.
  • Symbal can detect systematic misalignments.
  • Symbal exhibits strong performance on SymbalBench.
  • Symbal identifies systematic misalignments in 63.8% of cases, a significant improvement over the closest baseline.

Evidence Used

  • The study discusses the focus on systematic misalignments and the development of Symbal for their detection.
  • Details about SymbalBench are provided, including its composition and purpose.
  • Results from evaluations on SymbalBench and real-world scenarios are presented.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.