Boosting of Thoughts: Trial-and-Error Problem Solving with Large
  Language Models
Sijia Chen Baochun Li Di Niu
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 exploring and
self-evaluating many trees of thoughts in order to acquire an ensemble of
trial-and-error reasoning experiences, which will serve as a new form of
prompting to solve the complex problem. Starting from a simple prompt without
requiring examples, BoT iteratively explores and evaluates a large collection
of reasoning steps, and more importantly, uses error analysis obtained from the
LLM on them to explicitly revise prompting, which in turn enhances reasoning
step generation, until a final answer is attained. Our experiments with GPT-4
and Llama2 across extensive complex mathematical problems demonstrate that BoT
consistently achieves higher or comparable problem-solving rates than other
advanced prompting approaches.