Both Text and Images Leaked! A Systematic Analysis of Multimodal LLM
  Data Contamination
Authors: name: Dingjie Song, name: Sicheng Lai, name: Shunian Chen, name: Lichao Sun, name: Benyou Wang

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 dataset 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 sensitive to varying degrees of contamination and can
highlight significant performance improvements due to leakage of the training
set of multimodal benchmarks. Furthermore, We also explore the possibility of
contamination originating from the pre-training phase of LLMs used by MLLMs and
the fine-tuning phase of MLLMs, offering new insights into the stages at which
contamination may be introduced.