A development team is fine-tuning a language model to generate responses that are both creative and contextually humorous. They find that it is extremely difficult for human annotators to write 'perfect' examples of witty responses from scratch. Given this challenge, why is a preference-based annotation method (where annotators rank several model-generated options) often more effective than a demonstration-based method (where annotators write ideal outputs)?
0
1
Tags
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
AI Training Strategy for Empathetic Dialogue
A development team is fine-tuning a language model to generate responses that are both creative and contextually humorous. They find that it is extremely difficult for human annotators to write 'perfect' examples of witty responses from scratch. Given this challenge, why is a preference-based annotation method (where annotators rank several model-generated options) often more effective than a demonstration-based method (where annotators write ideal outputs)?
Annotation Strategy for Ethical AI