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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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Examples of Instruction-Following Tasks in SFT Datasets
A development team has fine-tuned a large language model to be a helpful assistant. They observe that the model excels at summarizing technical documents and answering direct factual questions, which were the primary tasks in its fine-tuning dataset. However, when users ask it to perform more creative tasks like writing a short poem or brainstorming marketing slogans, the model's performance is poor and generic. Which of the following strategies would be the most effective next step to improve the model's ability to handle this wider range of user requests?
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