LLMs作为数学与科学问题的潜在头脑风暴伙伴
LLMs as Potential Brainstorming Partners for Math and Science Problems
AI总结：
本文探讨了大型语言模型（如GPT-4）作为数学与科学问题头脑风暴伙伴的潜力，通过案例研究评估其协作能力与局限，旨在弥合人机智力协作的鸿沟。
摘要：
随着近期广泛成功的深度学习模型的兴起，数学和科学界的专业人士越来越有兴趣观察和评估最先进模型在协作寻找或解决通常需要创造力并因此需要头脑风暴的问题方面的能力。尽管当前人机智力协作与解决复杂数学和科学问题（例如六个未解决的千禧年大奖难题）之间仍存在巨大鸿沟，我们对此问题的初步调查揭示了弥合这一鸿沟的有希望的一步。这得益于大型语言模型（LLMs）的最新进展。更具体地说，我们进行了全面的案例研究，以探索当前最先进的LLM，特别是GPT-4，在与人类集体头脑风暴中的能力和局限性。
英文摘要：
With the recent rise of widely successful deep learning models, there is emerging interest among professionals in various math and science communities to see and evaluate the state-of-the-art models' abilities to collaborate on finding or solving problems that often require creativity and thus brainstorming. While a significant chasm still exists between current human-machine intellectual collaborations and the resolution of complex math and science problems, such as the six unsolved Millennium Prize Problems, our initial investigation into this matter reveals a promising step towards bridging the divide. This is due to the recent advancements in Large Language Models (LLMs). More specifically, we conduct comprehensive case studies to explore both the capabilities and limitations of the current state-of-the-art LLM, notably GPT-4, in collective brainstorming with humans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
