語言模型可以是邏輯求解器。

摘要

邏輯推理是人類智能的基本要素，也是問題解決和決策等任務的關鍵組成部分。最近的進展使得大型語言模型（LLMs）有可能展現推理能力，但複雜的邏輯推理仍然是一個挑戰。目前最先進的求解器增強語言模型使用LLMs首先解析自然語言的邏輯問題為符號表示，然後採用外部邏輯求解器來處理這些符號表示並輸出答案。儘管它們表現出色，但任何解析錯誤都不可避免地導致外部邏輯求解器執行失敗，無法回答邏輯問題。本文介紹了LoGiPT，一種新型語言模型，直接模擬邏輯求解器的推理過程，並通過學習嚴格遵循求解器語法和規則來避免解析錯誤。LoGiPT在一個新建的指導調整數據集上進行微調，該數據集揭示並完善了演繹求解器隱藏的推理過程。在兩個公開的演繹推理數據集上的實驗結果表明，LoGiPT在競爭性LLMs（如ChatGPT或GPT-4）上優於最先進的求解器增強語言模型和少數提示方法。

English

Logical reasoning is a fundamental aspect of human intelligence and a key component of tasks like problem-solving and decision-making. Recent advancements have enabled Large Language Models (LLMs) to potentially exhibit reasoning capabilities, but complex logical reasoning remains a challenge. The state-of-the-art, solver-augmented language models, use LLMs to parse natural language logical questions into symbolic representations first and then adopt external logical solvers to take in the symbolic representations and output the answers. Despite their impressive performance, any parsing errors will inevitably result in the failure of the execution of the external logical solver and no answer to the logical questions. In this paper, we introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers and bypasses the parsing errors by learning to strict adherence to solver syntax and grammar. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers. Experimental results on two public deductive reasoning datasets demonstrate that LoGiPT outperforms state-of-the-art solver-augmented LMs and few-shot prompting methods on competitive LLMs like ChatGPT or GPT-4.

語言模型可以是邏輯求解器。

Language Models can be Logical Solvers

摘要

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