Complex visual reasoning remains a key challenge today. Typically, the challenge is tackled using methodologies such as Chain of Thought (COT) and visual instruction tuning. However, how to organically combine these two methodologies for greater success remains unexplored. Also, issues like hallucinations and high training cost still need to be addressed. In this work, we devise an innovative multi-round training and reasoning framework suitable for lightweight Multimodal Large Language Models (MLLMs). Our self-questioning approach heuristically guides MLLMs to focus on visual clues relevant to the target problem, reducing hallucinations and enhancing the model's ability to describe fine-grained image details. This ultimately enables the model to perform well in complex visual reasoning and question-answering tasks. We have named this framework Socratic Questioning(SQ). To facilitate future research, we create a multimodal mini-dataset named CapQA, which includes 1k images of fine-grained activities, for visual instruction tuning and evaluation, our proposed SQ method leads to a 31.2% improvement in the hallucination score. Our extensive experiments on various benchmarks demonstrate SQ's remarkable capabilities in heuristic self-questioning, zero-shot visual reasoning and hallucination mitigation. Our model and code will be publicly available.

本研究解决了复杂视觉推理中的主要挑战，即如何有机结合链式思维和视觉指令调优的方法。通过提出一种创新的多轮训练和推理框架，名为苏格拉底式提问（SQ），该方法指引多模态大型语言模型关注与目标问题相关的视觉线索，成功减少了幻觉现象并提高了模型在复杂视觉推理及问答任务中的表现，从而推动了未来相关研究的发展。

苏格拉底式提问：学习在真实环境中自我引导多模态推理