  Recently developed large language models (LLMs) have been shown to perform
remarkably well on a wide range of language understanding tasks. But, can they
really "reason" over the natural language? This question has been receiving
significant research attention and many reasoning skills such as commonsense,
numerical, and qualitative have been studied. However, the crucial skill
pertaining to 'logical reasoning' has remained underexplored. Existing work
investigating this reasoning ability of LLMs has focused only on a couple of
inference rules (such as modus ponens and modus tollens) of propositional and
first-order logic. Addressing the above limitation, we comprehensively evaluate
the logical reasoning ability of LLMs on 25 different reasoning patterns
spanning over propositional, first-order, and non-monotonic logics. To enable
systematic evaluation, we introduce LogicBench, a natural language
question-answering dataset focusing on the use of a single inference rule. We
conduct detailed analysis with a range of LLMs such as GPT-4, ChatGPT, Gemini,
Llama-2, and Mistral using chain-of-thought prompting. Experimental results
show that existing LLMs do not fare well on LogicBench; especially, they
struggle with instances involving complex reasoning and negations. Furthermore,
they sometimes overlook contextual information necessary for reasoning to
arrive at the correct conclusion. We believe that our work and findings
facilitate future research for evaluating and enhancing the logical reasoning
ability of LLMs. Data and code are available at
https://github.com/Mihir3009/LogicBench.
