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arXiv · 2308.08614

Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought

Bin Lei·pei-Hung Lin·Chunhua Liao·Caiwen Ding
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Abstract

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 $15\%$.

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Bin Lei, pei-Hung Lin, Chunhua Liao, Caiwen Ding. 2023-08-16. Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought. https://arxiv.org/abs/2308.08614

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