Empowering LLMs with Logical Reasoning: A Comprehensive Survey

TL;DR

This survey reviews recent advances in enhancing large language models' logical reasoning abilities, addressing challenges in logical question answering and consistency, and discusses datasets, metrics, and future research directions.

Contribution

It provides a comprehensive taxonomy of methods, analyzes logical reasoning challenges, and explores promising future research avenues for LLMs.

Findings

Categorizes methods based on external solvers, prompts, and fine-tuning.

Identifies key challenges in logical question answering and consistency.

Reviews datasets and evaluation metrics for logical reasoning in LLMs.

Abstract

Large language models (LLMs) have achieved remarkable successes on various tasks. However, recent studies have found that there are still significant challenges to the logical reasoning abilities of LLMs, which can be categorized into the following two aspects: (1) Logical question answering: LLMs often fail to generate the correct answer within a complex logical problem which requires sophisticated deductive, inductive or abductive reasoning given a collection of premises. (2) Logical consistency: LLMs are prone to producing responses contradicting themselves across different questions. For example, a state-of-the-art question-answering LLM Macaw, answers Yes to both questions Is a magpie a bird? and Does a bird have wings? but answers No to Does a magpie have wings?. To facilitate this research direction, we comprehensively investigate the most cutting-edge methods and propose a detailed taxonomy. Specifically, to accurately answer complex logic questions, previous methods can be categorized based on reliance on external solvers, prompts, and fine-tuning. To avoid logical contradictions, we discuss concepts and solutions of various logical consistencies, including implication, negation, transitivity, factuality consistencies, and their composites. In addition, we review commonly used benchmark datasets and evaluation metrics, and discuss promising research directions, such as extending to modal logic to account for uncertainty and developing efficient algorithms that simultaneously satisfy multiple logical consistencies.