This article is part of a series on Artificial Intelligence.

Language models are a class of artificial intelligence systems designed to process, understand, and generate human language. These models are foundational to the field of Natural Language Processing (NLP), enabling tasks such as text classification, machine translation, question answering, and information retrieval.

Traditionally, language models have been categorized into statistical, neural, and hybrid approaches. Classical language models include rule-based systems, n-gram models, and probabilistic methods, while more recent developments feature lightweight neural networks capable of delivering high performance without the resource demands of large-scale architectures.

Classical Statistical Language Models

Before the advent of neural networks, language modeling relied on statistical approaches. One of the earliest and most influential techniques was the n-gram model, which estimates the probability of a word based on the preceding n−1 words.

For example, in a bigram model (n = 2), the probability of a word depends only on the previous word. These models are simple, efficient, and interpretable, but suffer from data sparsity and limited context.

More advanced statistical models include:

  • Hidden Markov Models (HMMs): Used extensively in speech recognition and part-of-speech tagging. HMMs model sequences where the system being modeled is assumed to follow a Markov process with hidden states.
  • Probabilistic Context-Free Grammars (PCFGs): Extend traditional grammars by associating probabilities with each production rule, enabling probabilistic parsing of sentences.

These models laid the groundwork for many early NLP applications.

Early Neural Language Models

The limitations of statistical models led to the development of neural language models. These models learn dense vector representations of words and contexts, enabling generalization beyond observed data.

Word Embeddings

Word embeddings are numerical vector representations of words in a continuous space where semantically similar words are mapped to nearby points. This representation enables machines to process textual input more meaningfully.

Notable models include:

  • word2vec: Developed by Mikolov et al. (2013), it introduced two architectures: Continuous Bag of Words (CBOW) and Skip-Gram. These models predict context from a target word or vice versa using shallow neural networks.
  • GloVe (Global Vectors): Introduced by Pennington et al., this model combines global matrix factorization and local context windowing to generate word vectors that capture both semantic and syntactic relationships.
  • ELMo (Embeddings from Language Models): Developed by Peters et al. (2018), ELMo introduced contextualized embeddings derived from a bidirectional LSTM trained on a language modeling objective. Unlike static embeddings, ELMo's word vectors change based on context.

These models marked a significant shift in NLP, allowing for transfer learning and better semantic understanding with relatively modest computational resources.

Compact Transformer-Based Models

While transformers are commonly associated with large language models, compact transformer architectures offer the benefits of deep contextual representation with significantly reduced computational overhead.

  • DistilBERT: A smaller version of BERT created using knowledge distillation. It retains ~97% of BERT's performance while being 60% faster and lighter, making it suitable for resource-constrained environments.
  • ALBERT (A Lite BERT): Introduces parameter reduction techniques such as cross-layer parameter sharing and factorized embedding parameterization, allowing for more scalable models without loss in accuracy.

These models are increasingly popular in mobile applications and real-time systems, where inference speed and model size are critical.

Applications of Classical and Small Language Models

Despite the attention given to large-scale models, small and classical language models remain crucial in many domains due to their transparency, efficiency, and ease of deployment. Common use cases include:

  • Spell checking and autocorrection in text editors
  • Speech recognition and part-of-speech tagging using HMMs
  • Sentiment analysis with bag-of-words or word embedding-based classifiers
  • Information retrieval and ranking using TF-IDF or probabilistic relevance models

Moreover, in educational and low-resource settings, classical models serve as interpretable and computationally feasible alternatives to larger architectures.

Word Vectors Summary

Word vectors are integral to many language models. Whether derived from neural embeddings or matrix factorization techniques, they enable machines to capture relationships such as analogies (Car - Wheels + Wings ≈ Airplane).

They serve as foundational input for both classical models and compact transformer architectures, bridging the gap between symbolic and sub-symbolic AI.

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References

  1. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. [Link]
  2. Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing (3rd ed.). [Link]
  3. Wikipedia: N-gram
  4. Wikipedia: word2vec
  5. Wikipedia: Hidden Markov Model
  6. Wikipedia: ELMo
  7. Wikipedia: GloVe
  8. Wikipedia: DistilBERT
  9. Wikipedia: ALBERT