Summary of Mindstar: Enhancing Math Reasoning in Pre-trained Llms at Inference Time, by Jikun Kang et al.
MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time
by Jikun Kang, Xin Zhe Li, Xi Chen, Amirreza Kazemi, Qianyi Sun, Boxing Chen, Dong Li, Xu He, Quan He, Feng Wen, Jianye Hao, Jun Yao
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach called MindStar (M) that enhances the reasoning abilities of Large Language Models (LLMs) on complex mathematical tasks. Current methods rely heavily on supervised fine-tuning or self-improvement techniques, which require high-quality datasets or substantial computational resources. M instead formulates reasoning tasks as searching problems and proposes two search ideas to identify optimal reasoning paths. The authors evaluate the M* framework on GSM8K and MATH datasets, comparing its performance with existing open-source LLMs like Llama-2-13B and Mistral-7B. Results show that M* significantly improves the reasoning abilities of these models, achieving comparable performance to GPT-3.5 and Grok-1 while reducing model size and computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand math problems better. Right now, big language models can solve many tasks, but they struggle with tricky math questions. Some people try to make them smarter by giving them lots of examples or letting them practice on their own. But this requires a lot of work and special training data. The new method, called MindStar, is different. It thinks about math problems like puzzles to be solved, rather than trying to learn from examples. The researchers tested MindStar with two big math datasets and compared it to other language models. They found that MindStar made the models much better at solving math problems! |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Llama » Supervised
