IWISDM: Assessing instruction following in multimodal models at scale

TL;DR

This paper introduces iWISDM, a comprehensive benchmark environment for evaluating multimodal models' ability to follow complex visual and language instructions, revealing significant gaps compared to human performance.

Contribution

The paper presents iWISDM, a new scalable environment and benchmark suite for assessing instruction-following in multimodal models across diverse, complex vision-language tasks.

Findings

iWISDM effectively evaluates instruction-following in multimodal models

Current models lag behind human performance in instruction adherence

The benchmark reveals significant gaps in multimodal model capabilities

Abstract

The ability to perform complex tasks from detailed instructions is a key to many remarkable achievements of our species. As humans, we are not only capable of performing a wide variety of tasks but also very complex ones that may entail hundreds or thousands of steps to complete. Large language models and their more recent multimodal counterparts that integrate textual and visual inputs have achieved unprecedented success in performing complex tasks. Yet, most existing benchmarks are largely confined to single-modality inputs (either text or vision), narrowing the scope of multimodal assessments, particularly for instruction-following in multimodal contexts. To bridge this gap, we introduce the instructed-Virtual VISual Decision Making (iWISDM) environment engineered to generate a limitless array of vision-language tasks of varying complexity. Using iWISDM, we compiled three distinct benchmarks of instruction following visual tasks across varying complexity levels and evaluated several newly developed multimodal models on these benchmarks. Our findings establish iWISDM as a robust benchmark for assessing the instructional adherence of both existing and emergent multimodal models and highlight a large gap between these models' ability to precisely follow instructions with that of humans.The code of iWISDM is available on GitHub at https://github.com/BashivanLab/iWISDM.