Language model

A language model is a probabilistic model of a natural language. 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.

Language models are useful for a variety of tasks, including speech recognition (helping prevent predictions of low-probability (e.g. nonsense) sequences), machine translation, natural language generation (generating more human-like text), optical character recognition, handwriting recognition, grammar induction, and information retrieval.

Large language models, currently their most advanced form, are a combination of larger datasets (frequently using words scraped from the public internet), feedforward neural networks, and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.

Exponential
Maximum entropy language models encode the relationship between a word and the n-gram history using feature functions. The equation is

$$ P(w_m \mid w_1,\ldots,w_{m-1}) = \frac{1}{Z(w_1,\ldots,w_{m-1})} \exp (a^T f(w_1,\ldots,w_m))$$

where $$Z(w_1,\ldots,w_{m-1})$$ is the partition function, $$a$$ is the parameter vector, and $$f(w_1,\ldots,w_m)$$ 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 $$a$$ or some form of regularization.

The log-bilinear model is another example of an exponential language model.

Recurrent neural network
Continuous representations or embeddings of words are produced in recurrent neural network-based language models (known also as continuous space language models). 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.

Large language models
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.

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 they see, some proposed models investigate the rate of learning, e.g., through inspection of learning curves.

Various data sets have been developed for use in evaluating 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
 * BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU (Massive Multitask Language Understanding), BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs. (LLaMa Benchmark)