Summary of Both Text and Images Leaked! a Systematic Analysis Of Multimodal Llm Data Contamination, by Dingjie Song et al.
Both Text and Images Leaked! A Systematic Analysis of Multimodal LLM Data Contamination
by Dingjie Song, Sicheng Lai, Shunian Chen, Lichao Sun, Benyou Wang
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MM-Detect framework is designed to detect data contamination in multimodal large language models (MLLMs), which have shown superior performance on various multimodal benchmarks. The issue of data contamination during training creates challenges in evaluating and comparing MLLM performance, as existing methods are less effective for MLLMs due to their varying modalities and multiple training phases. MM-Detect is a multimodal data contamination detection framework specifically designed for MLLMs, which has been shown to be effective and sensitive in identifying different levels of contamination. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new method called MM-Detect that can help identify when language models are learning from the wrong data. This is important because it can affect how well the model performs on certain tasks. They tested their method on several multimodal benchmarks and found that it was good at detecting different levels of contamination. This could lead to better performance in the future. |
