New R-Hack published: n-grams in R — a small idea behind language models Before LLMs, language mode
| Source: Mastodon | Original article
A short tutorial titled **“n‑grams in R – a small idea behind language models”** has just been posted to the R‑Hack blog, timed to precede the next R‑Ladies Rome meetup. The author walks readers through creating n‑grams from a cleaned text corpus, turning raw word sequences into frequency tables and probability estimates with base R and tidyverse tools. A single script builds a term‑frequency matrix, demonstrates how to slide a window of n tokens over sentences, and visualises the most common bi‑grams and tri‑grams. The post also sketches how these counts can be turned into a simple predictive model – the very mechanism that underpinned early statistical language modelling before the rise of transformer‑based large language models (LLMs).
Why it matters is twofold. First, n‑grams remain the most transparent baseline for text mining, offering a clear, interpretable link between raw data and probability estimates. For data scientists who work with limited corpora, regulatory constraints or need explainable outputs, the approach is still competitive. Second, the tutorial lowers the barrier for R users—particularly in the Nordic data‑science community, where R enjoys strong adoption in academia and public‑sector analytics—to experiment with language‑model fundamentals without switching to Python or heavyweight deep‑learning frameworks. By grounding practitioners in the statistical roots of modern LLMs, the hack helps demystify the “black‑box” narrative that often surrounds generative AI.
Looking ahead, the R‑Ladies Rome session will likely expand the discussion to downstream tasks such as sentiment scoring and simple next‑word prediction, and may spark community contributions to R packages like **tidytext** or **quanteda** that streamline n‑gram pipelines. Keep an eye on whether Nordic research groups adopt the tutorial for teaching introductory NLP in university courses, and whether any open‑source projects emerge that combine these lightweight n‑gram models with recent serverless inference tools such as Amazon SageMaker’s custom endpoints—a trend we noted in our coverage of AI tooling on 6 April. The convergence of classic statistical methods and modern deployment stacks could revive n‑grams as a fast‑prototype layer beneath larger transformer systems.
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