LLMs as Potential Brainstorming Partners for Math and Science Problems (2310.10677v1)
Abstract: 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 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.
- Scientific discovery and the environment.
- Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.
- Deep blue. Artificial intelligence, 134(1-2):57–83.
- Advancing mathematics by guiding human intuition with ai. Nature, 600(7887):70–74.
- Mathematical capabilities of chatgpt. arXiv preprint arXiv:2301.13867.
- I can’t believe there’s no images! learning visual tasks using only language data. arXiv preprint arXiv:2211.09778.
- Clay Mathematics Institute. 2023. The millennium prize problems.
- Pushmeet Kohli. 2023. The potential of ai in advancing science and the importance of ensuring ai’s responsible use.
- Is human led mathematics over? panel with joelle pineau, timothy gowers and yann lecun.
- OpenAI. 2023. Gpt-4 technical report.
- Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR.
- Enhancing the transformer with explicit relational encoding for math problem solving. arXiv preprint arXiv:1910.06611.
- AlphaFold Team. 2021. Alphafold: A solution to a 50-year-old grand challenge in biology.
- Anne Trafton. 2020. Artificial intelligence yields new antibiotic.
- Human-ai collaboration in data science: Exploring data scientists’ perceptions of automated ai. Proceedings of the ACM on human-computer interaction, 3(CSCW):1–24.
- Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837.
- Hongming Zhang and Tianyang Yu. 2020. Alphazero. Deep Reinforcement Learning: Fundamentals, Research and Applications, pages 391–415.
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