Welcome to Lecture 24 of the course "Large Language Models" by Prof. Mitesh M.Khapra.
Full Course: https://study.iitm.ac.in/ds/course_pages/BSCS5001.html
Video Overview
In this lecture, we break down the original BERT model’s training objectives: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). You’ll learn how MLM helps BERT predict masked words within a sentence and how NSP was designed to capture relationships between consecutive sentences.
We will also explore how segment embeddings and positional embeddings are learned, and the role they play in enabling downstream NLP tasks. Finally, we’ll discuss why NSP was later dropped in many subsequent models, and what this shift meant for modern transformer architectures.
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