Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries. However, when facing complex problems that require multi-step logical reasoning, their accuracy dramatically decreases. Current research has explored the realm of \textit{prompting engineering} to bolster the inferential capacities of these models. Our paper unveils a pioneering prompting technique, dubbed \textit{Graph of Thoughts (GoT)}. Through testing on a trio of escalating challenges: the 24-point game, resolution of high-degree polynomial equations, and derivation of formulas for recursive sequences, our method outperformed GPT-4, achieving accuracy improvements of 89.7%, 86%, and 56% for each respective task. Moreover, when juxtaposed with the state-of-the-art (SOTA) prompting method, \textit{Tree of Thought (ToT)}, our approach registered an average accuracy boost of 23%, 24%, and 15%.
@article{arxiv.2308.08614,
title = {Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought},
author = {Bin Lei and pei-Hung Lin and Chunhua Liao and Caiwen Ding},
journal= {arXiv preprint arXiv:2308.08614},
year = {2023}
}