{
  "id": "2308.08614",
  "version": "v1",
  "published": "2023-08-16T18:13:27.000Z",
  "updated": "2023-08-16T18:13:27.000Z",
  "title": "Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought",
  "authors": [
    "Bin Lei",
    "pei-Hung Lin",
    "Chunhua Liao",
    "Caiwen Ding"
  ],
  "categories": [
    "cs.LG",
    "cs.AI",
    "cs.CL"
  ],
  "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 $15\\%$.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-08-16T18:13:27.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "large language models",
      "boosting logical reasoning",
      "average accuracy boost",
      "high-degree polynomial equations",
      "addressing standard queries"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}