MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs
<|reference_start|>MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs: We introduce MIA-Bench, a new benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to strictly adhere to complex instructions. Our benchmark comprises a diverse set of 400 image-prompt pairs, each crafted to challenge the models' compliance with layered instructions in generating accurate responses that satisfy specific requested patterns. Evaluation results from a wide array of state-of-the-art MLLMs reveal significant variations in performance, highlighting areas for improvement in instruction fidelity. Additionally, we create extra training data and explore supervised fine-tuning to enhance the models' ability to strictly follow instructions without compromising performance on other tasks. We hope this benchmark not only serves as a tool for measuring MLLM adherence to instructions, but also guides future developments in MLLM training methods.<|reference_end|>
@article{qian2024mia-bench:,
    title={MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal
  LLMs},
    author={Yusu Qian, Hanrong Ye, Jean-Philippe Fauconnier, Peter Grasch, Yinfei
  Yang, Zhe Gan},
    journal={arXiv preprint arXiv:2407.01509},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01509},
    primaryClass={cs.CV cs.CL}
}