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Modern NLP Applications of the Predict-then-Refine Paradigm
In the era of deep learning, the predict-then-refine paradigm is commonly applied to sequence-to-sequence problems. Tasks such as grammar correction and text rewriting often follow this theme, where an initial sequence is generated and then refined to improve its quality and correctness.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
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Brill's Tagger as an Early Example of Predict-then-Refine
Modern NLP Applications of the Predict-then-Refine Paradigm
Self-Refinement in LLMs
An AI-powered code completion tool is designed to help developers write functions. When a developer provides a function name and a comment describing its purpose, the tool first generates a complete, functional block of code. Following this initial generation, the tool enters a loop where it analyzes the code it just wrote, identifies potential inefficiencies or non-standard practices, and applies a specific correction. This analysis-and-correction loop repeats several times, with the code block
Distinguishing NLP System Architectures
Analyzing System Architectures for Output Generation
Sequential Scaling
Learn After
An engineering team is building a system to summarize long technical documents. They are considering several architectures. Which of the following designs best exemplifies a 'predict-then-refine' approach for a sequence-to-sequence task?
Improving a Machine Translation System
Evaluating Architectural Choices for Text Style Transfer