Data Sourcing Strategy for Chatbot Fine-Tuning
A project manager is deciding between two data sourcing strategies for fine-tuning a new customer service chatbot. The first option is to hire a small team of experts to manually create 20,000 high-quality question-answer pairs. The second option is to programmatically collect and filter 200,000 pairs from a variety of public Q&A websites. The manager chooses the second option. Justify this decision by explaining two distinct advantages that the larger, web-sourced dataset provides for training a robust and versatile model.
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Ch.4 Alignment - 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
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A development team is fine-tuning a model to be a general-purpose, open-domain question-answering assistant. They are considering two approaches for creating the training dataset:
- Having a small, dedicated team of experts write 10,000 high-quality question-answer pairs.
- Programmatically collecting and filtering 100,000 question-answer pairs from various public Q&A websites.
Which approach is more likely to result in a model that can handle a wider variety of unanticipated user questions, and what is the primary reason?
Data Sourcing Strategy for Chatbot Fine-Tuning
Evaluating a Data Sourcing Strategy for a Niche Chatbot