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 t
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