Both Text and Images Leaked! A Systematic Analysis of Data Contamination in Multimodal LLM

ACL ARR 2025 February Submission687 Authors

Abstract: The rapid progression of multimodal large language models (MLLMs) has demonstrated superior performance on various multimodal benchmarks. However, the issue of data contamination during training creates challenges in performance evaluation and comparison. While numerous methods exist for detecting models' contamination in large language models (LLMs), they are less effective for MLLMs due to their various modalities and multiple training phases. In this study, we introduce a multimodal data contamination detection framework, MM-Detect, designed for MLLMs. Our experimental results indicate that MM-Detect is quite effective and sensitive in identifying varying degrees of contamination, and can highlight significant performance improvements due to the leakage of multimodal benchmark training sets. Furthermore, we explore whether the contamination originates from the base LLMs used by MLLMs or the multimodal training phase, providing new insights into the stages at which contamination may be introduced.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: Vision question answering, Evaluation, Analysis, Robustness, Data contamination

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models, Data analysis

Languages Studied: English

Submission Number: 687