Dissecting Dissonance: Benchmarking Large Multimodal Models Against Self-Contradictory Instructions
Jin Gao, Lei Gan, Yuankai Li, Yixin Ye, Dequan Wang*
Abstract
"Large multimodal models (LMMs) excel in adhering to human instructions. However, self-contradictory instructions may arise due to the increasing trend of multimodal interaction and context length, which is challenging for language beginners and vulnerable populations. We introduce the Self-Contradictory Instructions benchmark to evaluate the capability of LMMs in recognizing conflicting commands. It comprises 20,000 conflicts, evenly distributed between language and vision paradigms. It is constructed by a novel automatic dataset creation framework, which expedites the process and enables us to encompass a wide range of instruction forms. Our comprehensive evaluation reveals current LMMs consistently struggle to identify multimodal instruction discordance due to a lack of self-awareness. Hence, we propose the Cognitive Awakening Prompting to inject cognition from external, largely enhancing dissonance detection. Here are our website, dataset, and code."
BibTeX
@inproceedings{eccv2024_dissectingdisson,
title = {Dissecting Dissonance: Benchmarking Large Multimodal Models Against Self-Contradictory Instructions},
author = {Jin Gao and Lei Gan and Yuankai Li and Yixin Ye and Dequan Wang*},
booktitle = {ECCV 2024},
year = {2024}
}