Document Type

Conference Proceeding

Publication Title

Proceedings of the Annual Meeting of the Association for Computational Linguistics

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 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.

First Page

3062

Last Page

3077

Publication Date

7-2023

Keywords

Compositionality, Deductive reasoning, Factual knowledge, Language model, Logic rules, Logical reasoning, Reasoning framework, Symbolic programming, Symbolic reasoning, Through the lens

Comments

Preprint version from arXiv

Uploaded on June 20, 2024

Download

Share

COinS