Minds versus Machines: Rethinking Entailment Verification with Language Models

Humans make numerous inferences in text comprehension to understand discourse. This paper aims to understand the commonalities and disparities in the inference judgments between humans and state-of-the-art Large Language Models (LLMs). Leveraging a comprehensively curated entailment verification benchmark, we evaluate both human and LLM performance across various reasoning categories. Our benchmark includes datasets from three categories (NLI, contextual QA, and rationales) that include multi-sentence premises and different knowledge types, thereby evaluating the inference capabilities in complex reasoning instances. Notably, our …

art benchmark cs.ai cs.cl discourse human humans inference inferences language language models large language large language models llm llm performance llms machines paper performance reasoning state text verification