# LLMs as Potential Brainstorming Partners for Math and Science Problems

arXiv: https://arxiv.org/abs/2310.10677 · HTML: https://mlsanity.com/paper/2310.10677/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving](https://mlsanity.com/paper/1910.06611/) (2019)

## Built on by (most cited first, 6 of 6)

- [Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://mlsanity.com/paper/2402.00157/) (2024)
- [How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs](https://mlsanity.com/paper/2410.13857/) (2024)
- [The Expressibility of Polynomial based Attention Scheme](https://mlsanity.com/paper/2310.20051/) (2023)
- [Language Models as Critical Thinking Tools: A Case Study of Philosophers](https://mlsanity.com/paper/2404.04516/) (2024)
- [Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models](https://mlsanity.com/paper/2407.02220/) (2024)
- [Thinkink: 2D Spatial Ink-native Interaction with LLMs](https://mlsanity.com/paper/2607.21468/) (2026)

## Abstract (arXiv)

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.