ANLI

Adversarial Natural Language Inference (ANLI) is a benchmark for evaluating whether natural language understanding systems possess genuine inference capabilities or merely exploit statistical patterns in their training data. Developed by Nie et al. at Facebook AI Research, ANLI is constructed through an iterative adversarial process: human annotators attempt to fool state-of-the-art models with carefully crafted examples, and the dataset evolves as models improve. This design makes ANLI a dynamic benchmark — one that resists the benchmark overfitting that saturates static evaluation sets.

The significance of ANLI extends beyond NLP evaluation. It represents a methodological shift from testing against a fixed target to testing against an adapting opponent — a shift that mirrors the structure of security analysis, where the adversary is assumed intelligent and adaptive. The ANLI construction protocol reveals that evaluating intelligence requires an evaluative process that itself learns, a principle with implications for adversarial evaluation across machine learning domains.