LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step
  Reasoning with Large Language Models
Shibo Hao Yi Gu Haotian Luo Tianyang Liu Xiyan Shao Xinyuan Wang Shuhua Xie Haodi Ma Adithya Samavedhi Qiyue Gao Zhen Wang Zhiting Hu
Generating accurate step-by-step reasoning is essential for Large Language
Models (LLMs) to address complex problems and enhance robustness and
interpretability. Despite the flux of research on developing advanced reasoning
approaches, systematically analyzing the diverse LLMs and reasoning strategies
in generating reasoning chains remains a significant challenge. The
difficulties stem from the lack of two key elements: (1) an automatic method
for evaluating the generated reasoning chains on different tasks, and (2) a
unified formalism and implementation of the diverse reasoning approaches for
systematic comparison. This paper aims to close the gap: (1) We introduce
AutoRace for fully automated reasoning chain evaluation. Existing metrics rely
on expensive human annotations or pre-defined LLM prompts not adaptable to
different tasks. In contrast, AutoRace automatically creates detailed
evaluation criteria tailored for each task, and uses GPT-4 for accurate
evaluation following the criteria. (2) We develop LLM Reasoners, a library for
standardized modular implementation of existing and new reasoning algorithms,
under a unified formulation of the search, reward, and world model components.
With the new evaluation and library, (3) we conduct extensive study of
different reasoning approaches (e.g., CoT, ToT, RAP). The analysis reveals
interesting findings about different factors contributing to reasoning,
including the reward-guidance, breadth-vs-depth in search, world model, and
prompt formats, etc.