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Instruction fine-tuning requires substantial engineering and experimental effort to achieve satisfactory results. Finding the optimal configuration involves conducting numerous fine-tuning runs and evaluations to experiment with hyperparameters like learning rate, batch size, and the number of training steps. Although this engineering cost is critically important and should not be overlooked, it remains significantly lower than the effort and expense required during the initial pre-training phase.

Engineering Effort in Instruction Fine-Tuning

Based on the provided case study, critically evaluate the startup's approach. Justify your assessment by identifying potential risks and explaining why this process is unlikely to yield an optimal result.

Evaluating a Fine-Tuning Strategy

A machine learning engineer is trying to optimize a language model for a specific task by adjusting its settings. Arrange the following steps into the logical, cyclical order that represents a single iteration of this experimental process.

A machine learning team fine-tunes a pre-trained language model on a new dataset of instructions. They use the default hyperparameters from a popular tutorial for a single training run. The resulting model performs poorly on their evaluation set. Based on the typical engineering process for this task, what is the most probable reason for the model's poor performance?

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