Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming

Hanlin Zhang, Ziyang Li, Jiani Huang, Mayur Naik, Eric Xing

Abstract: Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming. We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module equipped with provenance generates top-k proofs by deductive reasoning. In contrast to works that rely on hand-crafted logic rules, our differentiable symbolic reasoning architecture efficiently learns weighted rules to further improve LMs. DSR-LM is scalable, interpretable, and allows easy integration of prior knowledge, thereby supporting extensive symbolic programming to robustly derive a logical conclusion. Our experiments show that DSR-LM leads to improved logical reasoning of pre-trained LMs and outperforms a spectrum of competitive baselines even under systematic distribution shifts on sequence lengths.