The text provides an overview of using language models as an alternative scoring method in Elasticsearch for information retrieval, contrasting with the default BM25 method. Language models, commonly used in Natural Language Processing, estimate the probability distribution of terms within a language, with the Unigram Language Model assuming term independence. In this context, each document in a collection is represented by its own language model, and documents are ranked based on their likelihood to generate a given query. The text explains how probabilities are estimated using term frequency and document length, with smoothing techniques like Jelinek-Mercer and Dirichlet smoothing applied to adjust these models by incorporating a background model, thus avoiding zero probabilities for unseen terms. Practical examples demonstrate how to implement these language models in Elasticsearch, including setting up indices and interpreting query scores, and the text concludes with a mention of future blogs intended to explore comparisons with other scoring methods.