Improved Logical Reasoning of Language Models via Differentiable
  Symbolic Programming
Hanlin Zhang Jiani Huang Ziyang Li Mayur Naik Eric Xing
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 performs
deductive reasoning. In contrast to works that rely on hand-crafted logic
rules, our differentiable symbolic reasoning framework efficiently learns
weighted rules and applies semantic loss 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. The results of our experiments suggest that DSR-LM improves the
logical reasoning abilities of pre-trained language models, resulting in a
significant increase in accuracy of over 20% on deductive reasoning benchmarks.
Furthermore, DSR-LM outperforms a variety of competitive baselines when faced
with systematic changes in sequence length.