MNLI

MNLI stands for Multi-Genre Natural Language Inference. It is a large-scale benchmark dataset designed to evaluate a model's ability to determine whether a premise entails, contradicts, or is neutral with respect to a given hypothesis. The data is drawn from multiple genres of text to test robustness to domain variation and linguistic style.

The dataset includes premises and hypotheses paired from about ten genres, such as fiction, government, travel, telephone conversations, and user-generated web text. Each example is labeled with one of three categories: entailment, neutral, and contradiction. The MNLI corpus provides two separate evaluation splits: matched (in-domain) and mismatched (out-of-domain).

When released by Williams, Nangia, and Bowman in 2018, MNLI quickly became a standard benchmark for natural language inference and transfer learning. It is widely used to train and evaluate neural models, especially those based on deep learning and transformer architectures. It has influenced the development of evaluation protocols in related benchmarks and is frequently used alongside other datasets in broader understanding tasks such as GLUE.

The dataset's scale and genre coverage make it a challenging test for cross-domain reasoning, but it has also exposed biases and annotation quirks common in large NLI corpora. Researchers continue to use MNLI as a baseline for model robustness and to motivate new approaches to reasoning and generalization.