Causal vs Masked LM - Deep Dive and Coding Problem

This article explores the differences between Causal Language Models (CLMs) and Masked Language Models (MLMs) in the context of pretraining Large Language Models (LLMs). It covers key concepts, practical applications, and the connection to the pretraining chapter.

Details

Causal Language Models (CLMs) are trained to predict the next word in a sequence, given the context of the previous words. This approach is based on the idea that language is inherently causal, where the current word depends on the previous words. Masked Language Models (MLMs), on the other hand, are trained to predict a randomly masked word in a sequence, given the context of the surrounding words. This approach is based on the idea that language is inherently masked, with some words unknown or missing. The choice between causal and masked LMs has significant implications for the performance of LLMs in real-world applications. CLMs are useful for tasks that require generating coherent and context-dependent text, while MLMs are useful for tasks that require understanding the context and relationships between words. Understanding the key concepts, practical applications, and connection to the pretraining chapter is crucial for designing effective pretraining strategies that can improve the performance of LLMs.