Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning

• URL: http://arxiv.org/abs/2404.12966v4
• Date: Tue, 19 Nov 2024 15:22:16 GMT
• Title: Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning
• Authors: Yian Li, Wentao Tian, Yang Jiao, Jingjing Chen, Na Zhao, Yu-Gang Jiang,
• Abstract summary: We show that most prevalent MLLMs can be easily fooled by the introduction of a presupposition into the question. We also propose a simple yet effective method, Active Deduction (AD), to encourage the model to actively perform composite deduction.
• Score: 68.83624133567213
• License:
• Abstract: Recently, Multimodal Large Language Models (MLLMs) have achieved significant success across multiple disciplines due to their exceptional instruction-following capabilities and extensive world knowledge. However, whether these MLLMs possess human-like compositional reasoning abilities remains an open problem. To unveil their reasoning behaviors, we first curate a \textbf{M}ultimodal \textbf{A}ssumptive \textbf{R}ea\textbf{s}oning Benchmark (MARS-Bench) in this paper. Interestingly, we find that most prevalent MLLMs can be easily fooled by the introduction of a presupposition into the question, whereas such presuppositions appear naive to human reasoning. Besides, we also propose a simple yet effective method, Active Deduction (AD), to encourage the model to actively perform composite deduction before reaching a final decision. Equipped with the proposed AD method, a MLLM demonstrates significant improvements in assumptive reasoning abilities without compromising its general-purpose question-answering performance. We also provide extensive evaluations of both open-source and private MLLMs on MARS-Bench, along with experimental analyses of the AD method.

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