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Supervised Fine-Tuning (SFT) is fundamentally different from pre-training due to its requirement for labeled data, whereas pre-training utilizes raw text that is readily and widely available. This dependency introduces significant challenges, as the tasks of data annotation and selection are complex, similar to other supervised machine learning domains.

SFT's Reliance on Labeled Data

Based on the data requirements for the fine-tuning process, what is the primary obstacle the startup must overcome before they can begin training their specialized chatbot? Explain why this is a necessary step.

Fine-Tuning for a Niche Domain

Contrast the data acquisition and preparation processes for a large language model's initial, general-purpose training phase with the subsequent phase where it is adapted for a specific, supervised task. In your analysis, explain why the data requirements for the second phase often present a more significant bottleneck than for the first.

Data Paradigms: Pre-training vs. Supervised Fine-Tuning

A research lab has a powerful, general-purpose language model that was trained on a vast, unlabeled corpus of internet text. They now want to adapt this model to perform a specialized task: accurately summarizing legal documents. Based on the typical data requirements for this adaptation process, what is the most significant and immediate challenge the lab will face?

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