Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models

arXiv:2402.11140v1 Announce Type: new
Abstract: The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thought demonstrations as exemplars in prompts. Recent work, e.g., Tree of Thoughts, has pointed out the importance of exploration and self-evaluation in reasoning step selection for complex problem solving. In this paper, we present Boosting of Thoughts (BoT), an automated prompting framework for problem solving with LLMs by iteratively …

abstract arxiv boosting chain of thought cs.cl error exploration importance language language models large language large language models llms performance prompting prompts reasoning thought thoughts tree type work