PCA-Bench: Evaluating Multimodal Large Language Models in
  Perception-Cognition-Action Chain
Liang Chen Yichi Zhang Shuhuai Ren Haozhe Zhao Zefan Cai Yuchi Wang Peiyi Wang Xiangdi Meng Tianyu Liu Baobao Chang
We present PCA-Bench, a multimodal decision-making benchmark for evaluating
the integrated capabilities of Multimodal Large Language Models (MLLMs).
Departing from previous benchmarks focusing on simplistic tasks and individual
model capability, PCA-Bench introduces three complex scenarios: autonomous
driving, domestic robotics, and open-world games. Given task instructions and
diverse contexts, the model is required to seamlessly integrate multiple
capabilities of Perception, Cognition, and Action in a reasoning chain to make
accurate decisions. Moreover, PCA-Bench features error localization
capabilities, scrutinizing model inaccuracies in areas such as perception,
knowledge, or reasoning. This enhances the reliability of deploying MLLMs. To
balance accuracy and efficiency in evaluation, we propose PCA-Eval, an
automatic evaluation protocol, and assess 10 prevalent MLLMs. The results
reveal significant performance disparities between open-source models and
powerful proprietary models like GPT-4 Vision. To address this, we introduce
Embodied-Instruction-Evolution (EIE), an automatic framework for synthesizing
instruction tuning examples in multimodal embodied environments. EIE generates
7,510 training examples in PCA-Bench and enhances the performance of
open-source MLLMs, occasionally surpassing GPT-4 Vision (+3\% in decision
accuracy), thereby validating the effectiveness of EIE. Our findings suggest
that robust MLLMs like GPT4-Vision show promise for decision-making in embodied
agents, opening new avenues for MLLM research.