在人工智能领域，多模态思维链推理是当前研究的热点之一。大型语言模型（LLMs）在自然语言处理（NLP）领域的突破性进展，使得这些模型能够理解和生成复杂的文本。然而，随着技术的发展和应用场景的多样化，单一文本模态的能力已经不能满足现代需求。研究者们开始尝试将文本CoT的能力扩展到多模态思维链推理领域，以应对更复杂和多样化的任务需求。
近日，哈工大赛尔实验室的陈麒光博士在ACL 2024 Oral会议上发表了一篇关于多模态思维链推理的研究论文。该研究揭示了当前多模态思维链基准存在的严重问题，包括视觉模态推理缺失、仅有单步视觉模态推理以及领域覆盖不足。这些问题严重制约了多模态思维链领域的发展。
为了解决这些问题，研究团队开发了一个新的基准——Multi-Domain Multi-step Multi-modal Chain-of-Thought（M3CoT）。该基准旨在解决上述问题，并推动多领域、多步和多模态思维链的进步。M3CoT基准通过全面的评估，涉及丰富的多模态推理设置与方法，旨在提供一个有价值的资源，为多领域、多步和多模态思维链的研究提供开创性的基础。
这项研究不仅揭示了多模态思维链推理领域的挑战，也为未来的研究指明了方向。随着科技的不断进步，多模态思维链推理有望在人工智能领域发挥更大的作用，推动相关技术的实际应用。
英语如下：
Title: “ACL 2024 Oral | Multimodal Thought Chains: The Next Step in AI Reasoning”
Keywords: Multimodal, Thought Chain, Artificial Intelligence
News Content: Title: Study Uncovers Challenges in Multimodal Thought Chain Reasoning, Advancing Field Development
In the field of artificial intelligence, multimodal thought chain reasoning is one of the current hot topics of research. The breakthrough progress in large language models (LLMs) in the natural language processing (NLP) domain has enabled these models to understand and generate complex text. However, with the advancement of technology and the diversification of application scenarios, the ability of a single text modality to meet modern demands is no longer sufficient. Researchers have begun to explore extending the CoT (Chain-of-Thought) capability of text to the domain of multimodal thought chain reasoning to meet the needs of more complex and diverse task requirements.
Recently, Dr. Chen Qikong from the SAIL Lab at Harbin Institute of Technology presented a research paper on multimodal thought chain reasoning at the ACL 2024 Oral conference. This study revealed serious issues with current multimodal thought chain benchmarks, including missing visual modal reasoning, single-step visual modal reasoning, and insufficient domain coverage. These issues severely restrict the development of the multimodal thought chain field.
To address these issues, the research team developed a new benchmark—Multi-Domain Multi-step Multi-modal Chain-of-Thought (M3CoT). This benchmark aims to solve the aforementioned problems and advance multimodal thought chain reasoning across multiple domains, steps, and modalities. The M3CoT benchmark provides a comprehensive evaluation involving rich multimodal reasoning setups and methods, aiming to serve as a valuable resource that lays groundbreaking groundwork for research in multimodal thought chains across multiple domains and steps.
This research not only uncovers the challenges in the field of multimodal thought chain reasoning but also points the way for future research. With continuous technological progress, multimodal thought chain reasoning has the potential to play a greater role in the field of artificial intelligence and drive the practical applications of related technologies.
【来源】https://www.jiqizhixin.com/articles/2024-08-11-5
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