Evaluating a Flawed Fine-Tuning Strategy
A development team is fine-tuning a general-purpose language model to specialize in generating code for a specific programming language. To achieve high performance quickly, they use a large, homogenous dataset of code from a single project, set a high learning rate, and train for a large number of epochs. Critique this strategy. Identify two major risks associated with this approach and, for each risk, recommend a specific technique to mitigate it, explaining how your recommendation would lead to a better-performing final 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
Evaluation in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Diagnosing and Correcting a Fine-Tuning Process
Evaluating a Flawed Fine-Tuning Strategy
A development team is fine-tuning a large, pre-trained language model on a specialized dataset of medical research papers. They observe that while the model's performance on medical queries is excellent, it has started to perform poorly on simple, general-knowledge questions it could previously answer correctly. Which of the following adjustments to the fine-tuning process is the most direct and effective strategy to address this degradation in general capabilities?
A machine learning engineer is fine-tuning a pre-trained language model and observes several undesirable behaviors. Match each observed behavior with the most appropriate mitigation strategy.