Do Large Language Models Understand Logic or Just Mimick Context?
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要