Do Large Language Models Understand Logic or Just Mimick Context?

TL;DR

This paper critically examines whether large language models genuinely understand logical reasoning or merely mimic context, revealing that their success largely depends on probabilistic pattern matching rather than true logical comprehension.

Contribution

The study introduces counterfactual testing to analyze LLM reasoning, demonstrating their lack of genuine logical understanding and highlighting limitations of in-context learning.

Findings

LLMs do not truly understand logical rules.

Alterations in context disrupt LLM outputs.

In-context learning mainly boosts answer likelihood.

Abstract

Over the past few years, the abilities of large language models (LLMs) have received extensive attention, which have performed exceptionally well in complicated scenarios such as logical reasoning and symbolic inference. A significant factor contributing to this progress is the benefit of in-context learning and few-shot prompting. However, the reasons behind the success of such models using contextual reasoning have not been fully explored. Do LLMs have understand logical rules to draw inferences, or do they ``guess'' the answers by learning a type of probabilistic mapping through context? This paper investigates the reasoning capabilities of LLMs on two logical reasoning datasets by using counterfactual methods to replace context text and modify logical concepts. Based on our analysis, it is found that LLMs do not truly understand logical rules; rather, in-context learning has simply enhanced the likelihood of these models arriving at the correct answers. If one alters certain words in the context text or changes the concepts of logical terms, the outputs of LLMs can be significantly disrupted, leading to counter-intuitive responses. This work provides critical insights into the limitations of LLMs, underscoring the need for more robust mechanisms to ensure reliable logical reasoning in LLMs.