Benchmarks generally consist of a dataset and corresponding evaluation metrics. The dataset provides text samples and annotations, while the metrics measure a model's performance on tasks like answering questions, text classification, and machine translation. These benchmarks are developed and maintained by academic institutions, research organizations, and industry players to track progress in the field. In addition to accuracy, the metrics can include throughput, energy efficiency, bias, trust, and sustainability.

In the present book, ten typical literatures about retrospective benchmarking published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on retrospective benchmarking. We hope this book can demonstrate advances in retrospective benchmarking as well as give references to the researchers, students and other related people.