Summary of Give Me a Hint: Can Llms Take a Hint to Solve Math Problems?, by Vansh Agrawal et al.
Give me a hint: Can LLMs take a hint to solve math problems?
by Vansh Agrawal, Pratham Singla, Amitoj Singh Miglani, Shivank Garg, Ayush Mangal
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method aims to enhance the problem-solving abilities of language models (LLMs) by providing “hints” for advanced mathematical problems. This approach draws inspiration from human math pedagogy and is tested against various LLMs on a diverse set of problems from the MATH dataset, including comparisons with one-shot, few-shot, and chain-of-thought prompting techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve the language model’s ability to solve advanced mathematical problems by providing helpful hints. The approach is inspired by how humans teach math and is tested on different types of problems. The results show that this method can be effective in helping language models solve complex math problems. |
Keywords
» Artificial intelligence » Few shot » Language model » One shot » Prompting
