Summary of Pca-bench: Evaluating Multimodal Large Language Models in Perception-cognition-action Chain, by Liang Chen and Yichi Zhang and Shuhuai Ren and Haozhe Zhao and Zefan Cai and Yuchi Wang and Peiyi Wang and Xiangdi Meng and Tianyu Liu and Baobao Chang
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain
by Liang Chen, Yichi Zhang, Shuhuai Ren, Haozhe Zhao, Zefan Cai, Yuchi Wang, Peiyi Wang, Xiangdi Meng, Tianyu Liu, Baobao Chang
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper introduces PCA-Bench, a benchmark for evaluating the integrated capabilities of Multimodal Large Language Models (MLLMs) in complex scenarios such as autonomous driving, domestic robotics, and open-world games. The benchmark requires models to seamlessly integrate perception, cognition, and action capabilities to make accurate decisions. Additionally, PCA-Bench features error localization capabilities, scrutinizing model inaccuracies in areas like perception, knowledge, or reasoning. The authors propose an automatic evaluation protocol called PCA-Eval to balance accuracy and efficiency in evaluation. They assess 10 prevalent MLLMs, including open-source models and powerful proprietary models like GPT-4 Vision. The results reveal significant performance disparities between the two types of models. To address this, the authors introduce Embodied-Instruction-Evolution (EIE), an automatic framework for synthesizing instruction tuning examples in multimodal embodied environments. EIE generates training examples that enhance the performance of open-source MLLMs, occasionally surpassing GPT-4 Vision (+3% in decision accuracy). The findings suggest that robust MLLMs like GPT4-Vision show promise for decision-making in embodied agents, opening new avenues for MLLM research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special test for big language models to see how well they can make decisions. It’s like a game where the model has to use its skills to decide what to do next. The test includes scenarios like driving a car or controlling a robot, and it checks if the model is making good choices. The authors also found that some models are much better than others at this task, and they came up with a way to help those models get even better. |
Keywords
» Artificial intelligence » Gpt » Instruction tuning » Pca
