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Crowdsourcing Data for Fine-Tuning

A direct method for creating a fine-tuning dataset, distinct from using pre-existing resources, is to crowdsource the data from a user base. A typical workflow involves collecting user inputs, such as questions, and then generating corresponding responses. These responses can either be provided manually or created by an LLM, after which they undergo manual annotation and correction. This approach is particularly valuable for capturing authentic user behavior and gathering data on a wide range of novel problems not covered by traditional NLP tasks.

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Updated 2026-05-01

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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