Dissecting Dissonance: Benchmarking Large Multimodal Models Against Self-Contradictory Instructions
<|reference_start|>Dissecting Dissonance: Benchmarking Large Multimodal Models Against Self-Contradictory Instructions: 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. The dataset and code are here: https://selfcontradiction.github.io/.<|reference_end|>
@article{gao2024dissecting,
    title={Dissecting Dissonance: Benchmarking Large Multimodal Models Against
  Self-Contradictory Instructions},
    author={Jin Gao, Lei Gan, Yuankai Li, Yixin Ye, Dequan Wang},
    journal={arXiv preprint arXiv:2408.01091},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01091},
    primaryClass={cs.AI}
}