Summary of Mmevalpro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation, by Jinsheng Huang et al.
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation
by Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper proposes MMEvalPro, a benchmark designed to evaluate Large Multimodal Models’ (LMMs) cross-modal understanding and reasoning abilities. LMMs often outperform Large Language Models (LLMs) on multiple-choice questions (MCQs), but existing benchmarks suffer from systematic biases. To address this issue, MMEvalPro uses a trilogy evaluation pipeline and more rigorous metrics to assess LMM performance. The benchmark comprises 2,138 question triplets, with two-thirds manually labeled by human experts and the rest sourced from existing benchmarks like MMMU, ScienceQA, and MathVista. Compared to existing benchmarks, MMEvalPro is more challenging for LLMs and LMMs, demonstrating a larger performance gap between the best LMM and human performance (31.73% vs 8.03%). The paper’s in-depth analysis justifies the trustworthiness of evaluation, highlighting its potential for advancing future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well computers understand information from different sources, like images and text. Right now, there are some big biases in these tests that make it hard to know if the results are really accurate. The new method, called MMEvalPro, tries to fix this problem by making the tests more challenging and fairer. It uses a special way of asking questions that involves multiple parts: an image, a question, and several possible answers. The paper also shows that computers that can understand language but not images are still pretty good at these new tests, which makes it harder to trust the results. |
