Summary of The Benefits Of a Concise Chain Of Thought on Problem-solving in Large Language Models, by Matthew Renze and Erhan Guven
The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models
by Matthew Renze, Erhan Guven
First submitted to arxiv on: 11 Jan 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 paper introduces Concise Chain-of-Thought (CCoT) prompting, a method that compares standard CoT and CCoT prompts to evaluate the impact of conciseness on response length and correct-answer accuracy using GPT-3.5 and GPT-4 with a multiple-choice question-and-answer (MCQA) benchmark. The results show that CCoT reduces average response length by 48.70% for both models while having a negligible impact on problem-solving performance, but incurs a performance penalty of 27.69% on math problems when using GPT-3.5 with CCoT. Overall, CCoT leads to an average per-token cost reduction of 22.67%. The authors provide code, data, and supplemental materials on GitHub for reproducibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CCoT is a new way to help AI models answer questions more efficiently. Researchers compared how well two types of prompts worked: standard CoT prompts and CCoT prompts that are shorter and more concise. They used powerful language models called GPT-3.5 and GPT-4, and tested them on multiple-choice questions. The results show that the shorter prompts actually make it easier for the AI to give answers, but might not be as good at solving math problems. |
Keywords
» Artificial intelligence » Gpt » Prompting » Token
