4 Ways to Train an LLM Explained Simply: From Causal LM to Token Classification

goyalshaliniuk · x · 2026-08-14

This article explains four common methods for training large language models (LLMs) in a simple way:

Causal Language Modeling: Predicts the next word based on previous words, helping models learn natural sentence flow and structure, like finishing someone's sentence.
Masked Language Modeling: Learns by guessing missing words in a sentence using surrounding context, improving overall language understanding, like solving a fill-in-the-blank quiz.
Text Classification Modeling: Predicts the overall category of a sentence (e.g., sentiment, topic) by comparing predictions with real labels, like sorting emails into folders.
Token Classification Modeling: Assigns labels to each word or subword (e.g., names, places, dates), like highlighting words with tags.

These methods form the backbone of modern LLMs, each playing a unique role in making AI smarter and more useful.

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