Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation

• URL: http://arxiv.org/abs/2401.10005v2
• Date: Thu, 18 Jul 2024 02:35:30 GMT
• Title: Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation
• Authors: Kohei Uehara, Nabarun Goswami, Hanqin Wang, Toshiaki Baba, Kohtaro Tanaka, Tomohiro Hashimoto, Kai Wang, Rei Ito, Takagi Naoya, Ryo Umagami, Yingyi Wen, Tanachai Anakewat, Tatsuya Harada,
• Abstract summary: This paper presents a novel approach to develop a large Vision-and-Language Models (VLMs) We introduce a system that can ask a question to acquire necessary knowledge, thereby enhancing the robustness and explicability of the reasoning process. The dataset covers a range of tasks, from common ones like caption generation to specialized VQA tasks that require expert knowledge.
• Score: 34.45251681923171
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing demand for intelligent systems capable of interpreting and reasoning about visual content requires the development of large Vision-and-Language Models (VLMs) that are not only accurate but also have explicit reasoning capabilities. This paper presents a novel approach to develop a VLM with the ability to conduct explicit reasoning based on visual content and textual instructions. We introduce a system that can ask a question to acquire necessary knowledge, thereby enhancing the robustness and explicability of the reasoning process. To this end, we developed a novel dataset generated by a Large Language Model (LLM), designed to promote chain-of-thought reasoning combined with a question-asking mechanism. The dataset covers a range of tasks, from common ones like caption generation to specialized VQA tasks that require expert knowledge. Furthermore, using the dataset we created, we fine-tuned an existing VLM. This training enabled the models to generate questions and perform iterative reasoning during inference. The results demonstrated a stride toward a more robust, accurate, and interpretable VLM, capable of reasoning explicitly and seeking information proactively when confronted with ambiguous visual input.

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