Summary of Boosting Of Thoughts: Trial-and-error Problem Solving with Large Language Models, by Sijia Chen et al.
Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models
by Sijia Chen, Baochun Li, Di Niu
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Boosting of Thoughts (BoT), an automated prompting framework for Large Language Models (LLMs) to solve complex problems. The approach iteratively explores and self-evaluates multiple reasoning steps, utilizing error analysis from the LLM to refine prompting. This process enhances reasoning step generation until a final answer is achieved. BoT outperforms advanced prompting approaches on extensive complex mathematical problems using GPT-4 and Llama2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps computers learn how to solve difficult math problems by giving them hints and feedback. It’s like having a conversation with the computer, but instead of talking, it’s exchanging ideas and mistakes. This new way of helping computers think, called Boosting of Thoughts, makes them better at solving complex problems. |
Keywords
* Artificial intelligence * Boosting * Gpt * Prompting
