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arXiv · 2411.12498

Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus

Terufumi Morishita·Gaku Morio·Atsuki Yamaguchi·Yasuhiro Sogawa
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Abstract

Large language models (LLMs) are capable of solving a wide range of tasks, yet they have struggled with reasoning. To address this, we propose $\textbf{Additional Logic Training (ALT)}$, which aims to enhance LLMs' reasoning capabilities by program-generated logical reasoning samples. We first establish principles for designing high-quality samples by integrating symbolic logic theory and previous empirical insights. Then, based on these principles, we construct a synthetic corpus named $\textbf{Formal Logic Deduction Diverse}$ ($\textbf{FLD}$$_{\times 2}$), comprising numerous samples of multi-step deduction with unknown facts, diverse reasoning rules, diverse linguistic expressions, and challenging distractors. Finally, we empirically show that ALT on FLD$_{\times2}$ substantially enhances the reasoning capabilities of state-of-the-art LLMs, including LLaMA-3.1-70B. Improvements include gains of up to 30 points on logical reasoning benchmarks, up to 10 points on math and coding benchmarks, and 5 points on the benchmark suite BBH.

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Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa. 2024-11-19. Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus. https://arxiv.org/abs/2411.12498

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