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Analyzing a Fine-Tuning Dataset's Limitations
A team fine-tunes a pre-trained language model using a large dataset where every entry is a simple question-and-answer pair, such as {'instruction': 'What is the capital of France?', 'response': 'Paris'}. After training, the model excels at answering factual questions but fails to follow more complex commands like 'Write a poem about the ocean' or 'Summarize this paragraph.' Analyze the provided data sample and explain why a dataset composed exclusively of such examples leads to this limited performance.
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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
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A development team has a large, pre-trained language model that is proficient at predicting the next word in a sentence but is not effective at following direct user commands. The team's goal is to adapt the model to function as a helpful assistant that can answer a wide variety of questions directly and accurately. Which of the following datasets would be most effective for adapting the model to this new role?
Diagnosing a Fine-Tuning Problem
Analyzing a Fine-Tuning Dataset's Limitations