A research team is fine-tuning a language model to be a highly accurate and safe legal assistant. They have two datasets available:
- Dataset X: 2,000,000 legal question-answer pairs automatically scraped from public internet forums. A spot-check reveals that approximately 30% of the answers contain factual inaccuracies or outdated information.
- Dataset Y: 75,000 legal question-answer pairs that have been carefully written, reviewed, and verified for accuracy by legal experts.
Which dataset should the team prioritize for fine-tuning to achieve the best performance for their specific goal, and what is the most compelling reason?
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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
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Data Strategy for a Customer Support Chatbot
A research team is fine-tuning a language model to be a highly accurate and safe legal assistant. They have two datasets available:
- Dataset X: 2,000,000 legal question-answer pairs automatically scraped from public internet forums. A spot-check reveals that approximately 30% of the answers contain factual inaccuracies or outdated information.
- Dataset Y: 75,000 legal question-answer pairs that have been carefully written, reviewed, and verified for accuracy by legal experts.
Which dataset should the team prioritize for fine-tuning to achieve the best performance for their specific goal, and what is the most compelling reason?
Impact of Data Quality on Fine-Tuning Sample Size
When fine-tuning a language model for a specialized task, the most effective strategy is always to maximize the sheer volume of training examples, even if it means including data that is noisy, inconsistent, or only loosely related to the target task.