Summary of Evaluating and Advancing Multimodal Large Language Models in Ability Lens, by Feng Chen et al.
Evaluating and Advancing Multimodal Large Language Models in Ability Lens
by Feng Chen, Chenhui Gou, Jing Liu, Yang Yang, Zhaoyang Li, Jiyuan Zhang, Zhenbang Sun, Bohan Zhuang, Qi Wu
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 A novel unified evaluation framework for multimodal large language models’ vision perception abilities is introduced in this work. The authors identify significant variability across existing benchmarks, which hinders comprehensive assessments of perception skills. To address this, AbilityLens, a benchmark that evaluates MLLMs across six key perception abilities, focusing on both accuracy and stability, is proposed. This framework allows for the identification of strengths and weaknesses of current models, highlighting performance gaps between open-source and closed-source models. Additionally, an online evaluation mode is introduced, which reveals interesting ability conflict and early convergence phenomena during MLLM training. The authors also design a simple model merging method that mitigates performance decline due to ability conflict. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to test how well language models can understand what they see. It’s like a report card for these models, showing which ones are good at certain tasks and which ones need improvement. The authors found that different tests were giving different results, making it hard to compare the models. To fix this, they created a new test called AbilityLens that looks at six important skills, such as recognizing objects or reading text. This helps us understand where each model is strong and weak. They also came up with a way to combine the best parts of each model’s abilities to make them better. |
