Mia-bench: Towards Better Instruction Following Evaluation Of Multimodal Llms

Qian Yusu, Ye Hanrong, Fauconnier Jean-philippe, Grasch Peter, Yang Yinfei, Gan Zhe. Arxiv 2024

[Paper]    
Fine Tuning Multimodal Models Pretraining Methods Prompting Training Techniques

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.

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