Example of Reframing Text Classification as Text Generation
An application of transforming NLP problems into text generation involves recasting text classification. Instead of training a model to output a specific class label, a large language model can be prompted to generate text that explicitly states the classification, thereby converting the classification problem into a generation task.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Example of Reframing Text Classification as Text Generation
Instruction-based Prompts
Few-Shot Learning
Alternative Prompt Formats for Machine Translation
Text Classification in NLP
Versatility of Prompt Templates
Grammaticality Judgment as a Binary Classification Task for LLMs
Formal Definition of LLM Inference
Illustrative Purpose of Prompting Examples
The paradigm of using Large Language Models (LLMs) allows for many different NLP tasks (e.g., translation, sentiment analysis) to be reframed as a text generation problem. What is the fundamental advantage of this approach over traditional methods that required building a separate, specifically trained model for each individual task?
Reframing a Traditional NLP Task
Choosing an NLP Development Strategy
Classification via Prompt Completion
Reframing Numerical Scoring as Text Generation
Learn After
Example of a Prompt for Classification via Completion
A developer is building a system to categorize user reviews as either 'Positive' or 'Negative'. A traditional approach would involve a model that outputs a single, predefined label (e.g., the word 'Positive'). How does reframing this task as a text generation problem for a large language model fundamentally change the model's expected output?
Reframing Review Classification
Prompt Design for Generative Classification