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In practice, the Supervised Fine-Tuning process is not a simple procedure but one that requires careful examination and optimization. Achieving effective results involves substantial engineering work and iterative experimentation to fine-tune various aspects of the training process.

Engineering and Experimental Effort in SFT Optimization

Critically evaluate this team's plan. What is the most significant risk in their approach, and why does it represent a misunderstanding of the practical effort required for successful fine-tuning?

Evaluating a Fine-Tuning Strategy

A machine learning team is tasked with fine-tuning a pre-trained language model for a specific customer service chatbot application. Arrange the following actions into a logical, iterative sequence that reflects an effective optimization process.

A development team is using supervised fine-tuning on a pre-trained language model to create a specialized legal document summarizer. After the initial training run, they find the model's summaries are factually accurate but often miss key nuances and critical legal arguments. Which of the following next steps best demonstrates the iterative experimentation and engineering effort central to optimizing the fine-tuning process?

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