Example

Examples of Instruction-Following Tasks in SFT Datasets

Supervised Fine-Tuning (SFT) datasets are composed of diverse instruction-response pairs to teach models a variety of tasks. Examples of such pairs include:

  • Summarization: Given an instruction to summarize an article and the article's text, the model should produce a concise summary.
  • Information Extraction: Given an instruction to extract financial figures from a report and the report's text, the model should output the specific figures like revenue and profit margin.
  • Classification: Given an instruction to classify an email and the email's text, the model should output the correct category, such as "Spam".
  • Problem-Solving: Given an instruction to provide a solution to a technical issue and a description of the issue, the model should generate helpful troubleshooting steps.

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Updated 2025-10-10

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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