mlsanityData to 7 Oct 2026

A large annotated corpus for learning natural language inference

arXiv 1508.05326 ↗First posted 21 Aug 2015cs.CLcited by 1,837

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Cited by 79 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.

Key facts. Cited by 1,837 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Aug 2015.

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Citing papers per year: 2017: 1; 2018: 0; 2019: 1; 2020: 0; 2021: 373; 2022: 369; 2023: 399; 2024: 309; 2025: 215; 2026: 170.

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