Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917726014930944 |
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| author | Uehara, Kohei Goswami, Nabarun Wang, Hanqin Baba, Toshiaki Tanaka, Kohtaro Hashimoto, Tomohiro Wang, Kai Ito, Rei Naoya, Takagi Umagami, Ryo Wen, Yingyi Anakewat, Tanachai Harada, Tatsuya |
| author_facet | Uehara, Kohei Goswami, Nabarun Wang, Hanqin Baba, Toshiaki Tanaka, Kohtaro Hashimoto, Tomohiro Wang, Kai Ito, Rei Naoya, Takagi Umagami, Ryo Wen, Yingyi Anakewat, Tanachai Harada, Tatsuya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10005 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation Uehara, Kohei Goswami, Nabarun Wang, Hanqin Baba, Toshiaki Tanaka, Kohtaro Hashimoto, Tomohiro Wang, Kai Ito, Rei Naoya, Takagi Umagami, Ryo Wen, Yingyi Anakewat, Tanachai Harada, Tatsuya Computer Vision and Pattern Recognition Computation and Language 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. |
| title | Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2401.10005 |