Summary of Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models, by Haoran Liao et al.
Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models
by Haoran Liao, Jidong Tian, Shaohua Hu, Hao He, Yaohui Jin
First submitted to arxiv on: 24 Feb 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 Problem Elaboration Prompting (PEP) approach enhances the mathematical capacities of large language models (LLMs) by decomposing and elucidating problem context before reasoning. This technique improves context modeling and parsing efficiency, leading to enhanced performances on various mathematical tasks. Specifically, PEP demonstrates improvements of 9.93% and 8.80% with the GPT-3.5 model on GSM8k through greedy decoding and self-consistency, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models struggle with complex tasks like math problems. This paper introduces a new way to help them called Problem Elaboration Prompting (PEP). PEP breaks down math problems into smaller parts before the model tries to solve it. This makes it easier for the model to understand what’s being asked and make better decisions. The results show that PEP works well, especially when dealing with distracting information. |
Keywords
* Artificial intelligence * Gpt * Parsing * Prompting
