Which technique is widely used in the development of large language models?

Which technique is widely used in the development of large language models?

• A. Supervised learning
• B. Self-supervised learning
• C. Mask learning
• D. Generative AI

Answer: (C)

Masked language modeling is the technique widely used to train large language models. This self-supervised approach masks out parts of the input text and trains the model to predict the missing tokens from the surrounding context. Because it doesn’t require labeled data, it can learn from vast amounts of unlabeled text, enabling the model to capture complex patterns in language, including syntax, semantics, and long-range dependencies. The bidirectional context obtained by looking at the surrounding tokens helps the model build rich representations that transfer well to a wide range of downstream tasks. While supervised learning relies on labeled data and generative AI describes what the model can do, masked language modeling provides a concrete, scalable pretraining objective that has proven highly effective for shaping strong, generalizable language understanding in large models.

Masked language modeling is the technique widely used to train large language models. This self-supervised approach masks out parts of the input text and trains the model to predict the missing tokens from the surrounding context. Because it doesn’t require labeled data, it can learn from vast amounts of unlabeled text, enabling the model to capture complex patterns in language, including syntax, semantics, and long-range dependencies. The bidirectional context obtained by looking at the surrounding tokens helps the model build rich representations that transfer well to a wide range of downstream tasks. While supervised learning relies on labeled data and generative AI describes what the model can do, masked language modeling provides a concrete, scalable pretraining objective that has proven highly effective for shaping strong, generalizable language understanding in large models.