LLMs as Potential Brainstorming Partners for Math and Science Problems
This addresses the need for better AI collaboration tools in math and science communities, but it is incremental as it builds on existing LLM advancements.
The paper investigates the potential of Large Language Models (LLMs), specifically GPT-4, as brainstorming partners for solving math and science problems that require creativity, finding promising steps towards bridging the human-machine collaboration gap.
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.