learning, e.g. through inspection of learning curves. Various data sets have been developed to use to evaluate language processing systems. These include: Corpus of Linguistic Acceptability GLUE benchmark Microsoft Research Paraphrase Corpus Multi-Genre Natural Language Inference Question Natural Language Inference Quora Question Pairs Recognizing Textual Entailment Semantic Textual Similarity Benchmark SQuAD question answering Test Stanford Sentiment Treebank Winograd NLI Criticism Although contemporary language models, such as GPT-3, can be shown to match human performance on some tasks, it is not clear they are plausible cognitive models. For instance, recurrent neural networks have been shown to learn patterns humans do not learn and fail to learn patterns that humans do learn. See also Cache language model Deep linguistic processing Factored language model Generative pre-trained transformer Katz's back-off model Language technology Natural language processing Statistical model Transformer Ethics of artificial intelligence Notes References Further reading Statistical natural language processing Markov models