MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs

1 Mins read

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.

Source link

Related posts

Derive meaningful and actionable operational insights from AWS Using Amazon Q Business

8 Mins read
As a customer, you rely on Amazon Web Services (AWS) expertise to be available and understand your specific environment and operations. Today,…

Amazon SageMaker unveils the Cohere Command R fine-tuning model

5 Mins read
AWS announced the availability of the Cohere Command R fine-tuning model on Amazon SageMaker. This latest addition to the SageMaker suite of…

Nvidia AI Releases BigVGAN v2: A State-of-the-Art Neural Vocoder Transforming Audio Synthesis

2 Mins read
In the rapidly developing field of audio synthesis, Nvidia has recently introduced BigVGAN v2. This neural vocoder breaks previous records for audio…



Leave a Reply

Your email address will not be published. Required fields are marked *