A development team fine-tunes a large, general-purpose language model to act as a specialized chatbot for a financial services company. The training data consists exclusively of question-answer pairs about stock trading, portfolio management, and market analysis. After fine-tuning, the team observes that while the model provides excellent, detailed answers to financial questions, it now struggles to answer simple, general knowledge questions (e.g., 'What is the tallest mountain in the world?') t
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Evaluation in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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A development team fine-tunes a large, general-purpose language model to act as a specialized chatbot for a financial services company. The training data consists exclusively of question-answer pairs about stock trading, portfolio management, and market analysis. After fine-tuning, the team observes that while the model provides excellent, detailed answers to financial questions, it now struggles to answer simple, general knowledge questions (e.g., 'What is the tallest mountain in the world?') t
Mechanism of Knowledge Internalization via Fine-Tuning
Analyzing a Failed Fine-Tuning Strategy