Alanguage model is a probabilistic model of a natural language.[1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.[2]
Aword n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. [9] It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words. If only one previous word was considered, it was called a bigram model; if two words, a trigram model; if n − 1 words, an n-gram model.[10] Special tokens were introduced to denote the start and end of a sentence and .
To prevent a zero probability being assigned to unseen words, each word's probability is slightly lower than its frequency count in a corpus. To calculate it, various methods were used, from simple "add-one" smoothing (assign a count of 1 to unseen n-grams, as an uninformative prior) to more sophisticated models, such as Good–Turing discountingorback-off models.
Maximum entropy language models encode the relationship between a word and the n-gram history using feature functions. The equation is
where is the partition function, is the parameter vector, and is the feature function. In the simplest case, the feature function is just an indicator of the presence of a certain n-gram. It is helpful to use a prior on or some form of regularization.
The log-bilinear model is another example of an exponential language model.
Skip-gram language model is an attempt at overcoming the data sparsity problem that preceding (i.e. word n-gram language model) faced. Words represented in an embedding vector were not necessarily consecutive anymore, but could leave gaps that are skipped over.[11]
Formally, a k-skip-n-gram is a length-n subsequence where the components occur at distance at most k from each other.
For example, in the input text:
the rain in Spain falls mainly on the plain
the set of 1-skip-2-grams includes all the bigrams (2-grams), and in addition the subsequences
the in, rain Spain, in falls, Spain mainly, falls on, mainly the, and on plain.
In skip-gram model, semantic relations between words are represented by linear combinations, capturing a form of compositionality. For example, in some such models, if v is the function that maps a word w to its n-d vector representation, then
where ≈ is made precise by stipulating that its right-hand side must be the nearest neighbor of the value of the left-hand side.[12][13]
Continuous representations or embeddings of words are produced in recurrent neural network-based language models (known also as continuous space language models).[14] Such continuous space embeddings help to alleviate the curse of dimensionality, which is the consequence of the number of possible sequences of words increasing exponentially with the size of the vocabulary, furtherly causing a data sparsity problem. Neural networks avoid this problem by representing words as non-linear combinations of weights in a neural net.[15]
Alarge language model (LLM) is a computational model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. Based on language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a computationally intensive self-supervised and semi-supervised training process.[16] LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word.[17]
LLMs are artificial neural networks that utilize the transformer architecture, invented in 2017. The largest and most capable LLMs, as of June 2024[update], are built with a decoder-only transformer-based architecture, which enables efficient processing and generation of large-scale text data.
Historically, up to 2020, fine-tuning was the primary method used to adapt a model for specific tasks. However, larger models such as GPT-3 have demonstrated the ability to achieve similar results through prompt engineering, which involves crafting specific input prompts to guide the model's responses.[18] These models acquire knowledge about syntax, semantics, and ontologies[19] inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on.[20]
Although sometimes matching human performance, it is not clear whether they are plausible cognitive models. At least for recurrent neural networks, it has been shown that they sometimes learn patterns that humans do not, but fail to learn patterns that humans typically do.[21]
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 they see, some proposed models investigate the rate of learning, e.g., through inspection of learning curves.[22]
Various data sets have been developed for use in evaluating language processing systems.[23] These include:
^Jurafsky, Dan; Martin, James H. (2021). "N-gram Language Models". Speech and Language Processing (3rd ed.). Archived from the original on 22 May 2022. Retrieved 24 May 2022.
^Andreas, Jacob, Andreas Vlachos, and Stephen Clark (2013). "Semantic parsing as machine translation"Archived 15 August 2020 at the Wayback Machine. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).
^Ponte, Jay M.; Croft, W. Bruce (1998). A language modeling approach to information retrieval. Proceedings of the 21st ACM SIGIR Conference. Melbourne, Australia: ACM. pp. 275–281. doi:10.1145/290941.291008.
^Hiemstra, Djoerd (1998). A linguistically motivated probabilistically model of information retrieval. Proceedings of the 2nd European conference on Research and Advanced Technology for Digital Libraries. LNCS, Springer. pp. 569–584. doi:10.1007/3-540-49653-X_34.
^Bengio, Yoshua; Ducharme, Réjean; Vincent, Pascal; Janvin, Christian (1 March 2003). "A neural probabilistic language model". The Journal of Machine Learning Research. 3: 1137–1155 – via ACM Digital Library.
^Jurafsky, Dan; Martin, James H. (7 January 2023). "N-gram Language Models". Speech and Language Processing(PDF) (3rd edition draft ed.). Retrieved 24 May 2022.
^Karlgren, Jussi; Schutze, Hinrich (2015), "Evaluating Learning Language Representations", International Conference of the Cross-Language Evaluation Forum, Lecture Notes in Computer Science, Springer International Publishing, pp. 254–260, doi:10.1007/978-3-319-64206-2_8, ISBN9783319642055
^Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina (10 October 2018). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". arXiv:1810.04805 [cs.CL].
^Aghaebrahimian, Ahmad (2017), "Quora Question Answer Dataset", Text, Speech, and Dialogue, Lecture Notes in Computer Science, vol. 10415, Springer International Publishing, pp. 66–73, doi:10.1007/978-3-319-64206-2_8, ISBN9783319642055
^Sammons, V.G.Vinod Vydiswaran, Dan Roth, Mark; Vydiswaran, V.G.; Roth, Dan. "Recognizing Textual Entailment"(PDF). Archived from the original(PDF) on 9 August 2017. Retrieved 24 February 2019.{{cite web}}: CS1 maint: multiple names: authors list (link)
J M Ponte; W B Croft (1998). "A Language Modeling Approach to Information Retrieval". Research and Development in Information Retrieval. pp. 275–281. CiteSeerX10.1.1.117.4237.
F Song; W B Croft (1999). "A General Language Model for Information Retrieval". Research and Development in Information Retrieval. pp. 279–280. CiteSeerX10.1.1.21.6467.
Chen, Stanley; Joshua Goodman (1998). An Empirical Study of Smoothing Techniques for Language Modeling (Technical report). Harvard University. CiteSeerX10.1.1.131.5458.