REALM: Retrieval-Augmented Language Model Pre-Training (Paper Explained)

Yannic Kilcher · Beginner ·📄 Research Papers Explained ·5y ago
#ai #tech #science Open Domain Question Answering is one of the most challenging tasks in NLP. When answering a question, the model is able to retrieve arbitrary documents from an indexed corpus to gather more information. REALM shows how Masked Language Modeling (MLM) pretraining can be used to train a retriever for relevant documents in an end-to-end fashion and improves over state-of-the-art by a significant margin. OUTLINE: 0:00 - Introduction & Overview 4:30 - World Knowledge in Language Models 8:15 - Masked Language Modeling for Latent Document Retrieval 14:50 - Problem Formulation 17:…
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Chapters (15)

Introduction & Overview
4:30 World Knowledge in Language Models
8:15 Masked Language Modeling for Latent Document Retrieval
14:50 Problem Formulation
17:30 Knowledge Retriever Model using MIPS
23:50 Question Answering Model
27:50 Architecture Recap
29:55 Analysis of the Loss Gradient
34:15 Initialization using the Inverse Cloze Task
41:40 Prohibiting Trivial Retrievals
44:05 Null Document
45:00 Salient Span Masking
50:15 My Idea on Salient Span Masking
51:50 Experimental Results and Ablations
57:30 Concrete Example from the Model

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