Summary of Arithmetic Reasoning with Llm: Prolog Generation & Permutation, by Xiaocheng Yang et al.
Arithmetic Reasoning with LLM: Prolog Generation & Permutation
by Xiaocheng Yang, Bingsen Chen, Yik-Cheung Tam
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 uses large language models (LLMs) to solve elementary school math problems by extracting predicates and generating symbolic formulas. This approach avoids cascaded calculation errors that can occur with Chain of Thought (CoT). The model is trained to generate Prolog programs, which are then used to solve mathematical questions. Experimental results show that this method outperforms CoT generation on the GSM8K benchmark across three LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can help people solve math problems! A team of researchers tried a new way to get these models to solve math questions by having them create formulas and then using a special computer program to do the calculations. This worked better than another method called Chain of Thought, which can sometimes make mistakes because it does too many calculations in a row. The new method is good for solving math problems and could be useful for people who need help with math. |
