A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning (2024)

Abstract: Logical reasoning has been an ongoing pursuit in the field of AI. Despite significant advancements made by large language models (LLMs), they still struggle with complex logical reasoning problems. To enhance reasoning performance, one promising direction is scalable oversight, which requires LLMs to identify their own errors and then improve by themselves. Various self-verification methods have been proposed in pursuit of this goal. Nevertheless, whether existing models understand their own errors well is still under investigation. In this paper, we take a closer look at the self-verification abilities of LLMs in the context of logical reasoning, focusing on their ability to identify logical fallacies accurately. We introduce a dataset, FALLACIES, containing 232 types of reasoning fallacies categorized in a hierarchical taxonomy. By conducting exhaustive experiments on FALLACIES, we obtain comprehensive and detailed analyses of a series of models on their verification abilities. Our main findings suggest that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self-verification methods. Drawing from these observations, we offer suggestions for future research and practical applications of self-verification methods.

## introduction logical reasoning is not only a crucial aspect of human intelligence but also one of the longterm pursuits of artificial intelligence @xcite . it is indispensable in intelligent systems, enabling problem-solving, decision-making, and critical thinking. large language models (llms) have recently achieved remarkable advancements in a wide range of tasks @xcite . being prompted appropriately, llms exhibit the ## fallacies this section outlines the design principles and process of constructing our dataset fallacies. ## conclusion in this paper, we take a closer look at the verification abilities of llms in logical reasoning. we collect a dataset containing 232 fallacies and propose a hierarchical taxonomy of fallacies. our main experimental finding is that most llms still struggle to identify fallacies in logical reasoning accurately. this implies that it may be overly optimistic to expect llms to be able to inherently identify errors and conduct self-verification reasoning, at least with respect to the current state of technology. therefore, researchers and practitioners should be more cautious in using self-verification methods. we call for more research to explore the potentials and limitations of self-verification methods to steer llms towards improved accuracy and reliability.