Data Acquisition Methods for Instruction Fine-Tuning

Data Selection and Filtering Methods for Fine-Tuning

Principle of Quality Over Quantity in Fine-Tuning Data

Impact of Data Quality on Fine-Tuning Sample Size

Example of a Large-Scale Fine-Tuning Dataset: FLAN

Computational Cost of Fine-Tuning with Large Datasets

A research lab has successfully developed a powerful, general-purpose language model. Their next goal is to make this model exceptionally good at following specific user commands and answering questions accurately. As they adopt the common strategy of further training the model on a collection of command-and-response examples, which of the following challenges will they most likely identify as the primary bottleneck to achieving their goal?

Startup's Chatbot Development Challenge

The Data-Centric Shift in Language Model Development