DetermLR：将基于LLM的逻辑推理从不确定性增强到确定性
DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy
AI总结：
针对LLM推理中结构适应性差、条件利用不精确及历史经验缺失的问题，提出DetermLR方法，将推理视为从不确定性到确定性的演变，通过前提分类、定量优先和推理记忆，在多个逻辑基准上以更少步骤取得更高准确率。
摘要：
大型语言模型（LLM）的最新进展彻底改变了推理任务的格局。为了增强LLM模拟人类推理的能力，先前的研究侧重于使用链、树或图等各种思维结构来建模推理步骤。然而，基于LLM的推理仍然面临以下挑战：（1）预设结构对不同任务的适应性有限；（2）利用已知条件推导新条件的精度不足；（3）对后续推理步骤的历史推理经验考虑不足。为此，我们提出DetermLR，这是一种新颖的视角，将推理过程重新思考为从不确定性到确定性的演变。首先，我们将已知条件分为两类：确定性前提和不确定性前提。这为推理过程提供了总体方向，并引导LLM将不确定数据逐步转化为确定性见解。随后，我们利用定量测量来优先探索更相关的前提以发现新见解。此外，我们通过推理记忆自动存储和提取可用前提和推理路径，为后续推理步骤保留历史推理细节。综合实验结果表明，DetermLR在各种逻辑推理基准上超越了所有基线：LogiQA、ProofWriter、FOLIO、PrOntoQA和LogicalDeduction。与之前的逐步推理方法相比，DetermLR以更少的推理步骤实现了更高的准确性，突显了其在解决逻辑推理任务方面的优越效率和有效性。
英文摘要：
Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior studies have focused on modeling reasoning steps using various thought structures like chains, trees, or graphs. However, LLM-based reasoning still encounters the following challenges: (1) Limited adaptability of preset structures to diverse tasks; (2) Insufficient precision in exploiting known conditions to derive new ones; and (3) Inadequate consideration of historical reasoning experiences for subsequent reasoning steps. To this end, we propose DetermLR, a novel perspective that rethinks the reasoning process as an evolution from indeterminacy to determinacy. First, we categorize known conditions into two types: determinate and indeterminate premises This provides an oveall direction for the reasoning process and guides LLMs in converting indeterminate data into progressively determinate insights. Subsequently, we leverage quantitative measurements to prioritize more relevant premises to explore new insights. Furthermore, we automate the storage and extraction of available premises and reasoning paths with reasoning memory, preserving historical reasoning details for subsequent reasoning steps. Comprehensive experimental results demonstrate that DetermLR surpasses all baselines on various logical reasoning benchmarks: LogiQA, ProofWriter, FOLIO, PrOntoQA, and LogicalDeduction. Compared to previous multi-step reasoning methods, DetermLR achieves higher accuracy with fewer reasoning steps, highlighting its superior efficiency and effectiveness in solving logical reasoning tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
