Language model benchmark

These tests are intended for comparing different models' capabilities in areas such as language understanding, generation, and reasoning.

These benchmarks are developed and maintained by academic institutions, research organizations, and industry players to track progress in the field.

Conversely, certain benchmarks may be used as a training set, such as the One Billion Word Benchmark, which in modern language is just the negative log likelihood loss on a pretraining set with 1 billion words.

[3] Indeed, the distinction between benchmark and dataset in language models became sharper after the rise of the pretraining paradigm.

With programming tasks, the answer can generally be checked by running unit tests, with an upper limit on runtime.

[6] For less well-formed tasks, where the output can be any sentence, there are the following commonly used scores: BLEU ROUGE, METEOR, NIST, word error rate, LEPOR, CIDEr,[7] SPICE,[8] etc.

Some benchmarks were designed specifically to test for processing continuous text that is very long.