Boosting Logical Reasoning in Large Language Models through a New
  Framework: The Graph of Thought
Bin Lei pei-Hung Lin Chunhua Liao Caiwen Ding
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 $15\%$.