Although sometimes matching human performance, it is not clear whether they are plausible [[Cognitive model|cognitive models]]. At least for recurrent neural networks, it has been shown that they sometimes learn patterns whichthat humans do not learn, but fail to learn patterns that humans typically do learn.<ref>{{Cite book|last1=Hornstein|first1=Norbert|url=https://books.google.com/books?id=XoxsDwAAQBAJ&dq=adger+%22goldilocks%22&pg=PA153|title=Syntactic Structures after 60 Years: The Impact of the Chomskyan Revolution in Linguistics|last2=Lasnik|first2=Howard|last3=Patel-Grosz|first3=Pritty|last4=Yang|first4=Charles|date=2018-01-09|publisher=Walter de Gruyter GmbH & Co KG|isbn=978-1-5015-0692-5|language=en|access-date=11 December 2021|archive-date=16 April 2023|archive-url=https://web.archive.org/web/20230416160343/https://books.google.com/books?id=XoxsDwAAQBAJ&dq=adger+%22goldilocks%22&pg=PA153|url-status=live}}</ref>
== Evaluation and benchmarks ==
Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language-oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typically intended to be dynamic and to learn from data itthey seessee, some proposed models investigate the rate of learning, e.g., through inspection of learning curves.<ref>{{Citation|last1=Karlgren|first1=Jussi|last2=Schutze|first2=Hinrich|chapter=Evaluating Learning Language Representations|date=2015|pages=254–260|publisher=Springer International Publishing|isbn=9783319642055|doi=10.1007/978-3-319-64206-2_8|title=International Conference of the Cross-Language Evaluation Forum|series=Lecture Notes in Computer Science}}</ref>
Various data sets have been developed tofor use toin evaluateevaluating language processing systems.<ref name=":0">{{cite arXiv|last1=Devlin|first1=Jacob|last2=Chang|first2=Ming-Wei|last3=Lee|first3=Kenton|last4=Toutanova|first4=Kristina|date=2018-10-10|title=BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding|eprint=1810.04805|class=cs.CL}}</ref> These include:
* Corpus of Linguistic Acceptability<ref>{{Cite web|url=https://nyu-mll.github.io/CoLA/|title=The Corpus of Linguistic Acceptability (CoLA)|website=nyu-mll.github.io|access-date=2019-02-25|archive-date=7 December 2020|archive-url=https://web.archive.org/web/20201207081834/https://nyu-mll.github.io/CoLA/|url-status=live}}</ref>
