NLU Benchmark

NLU benchmarks are standardized datasets used to evaluate the performance of natural language understanding (NLU) models, aiming to objectively measure progress in the field and identify areas for improvement. Current research focuses on mitigating biases stemming from annotation instructions and dataset construction, exploring the impact of input data characteristics (like character-level information) on model performance, and developing more comprehensive benchmarks that assess a wider range of linguistic phenomena. These efforts are crucial for ensuring the reliability and validity of NLU model evaluations, ultimately leading to more robust and generalizable systems with broader practical applications.