Summary:
This article discusses the strategy of instruction-based finetuning to improve the performance of language models. It explains how instruction finetuning works and its benefits in controlling the behavior of the model. The article also explores different methods of obtaining datasets for instruction finetuning, including human-created and LLM-generated datasets. It highlights the Self-Instruct and Backtranslation methods for generating LLM-based datasets. The article mentions the NeurIPS LLM Efficiency Challenge and its rules regarding dataset usage. It also introduces the LIMA dataset, a high-quality human-generated dataset used for instruction finetuning. The article concludes by providing instructions on finetuning open-source LLMs using different datasets and suggesting research directions for further improvement.
https://sebastianraschka.com/blog/2023/optimizing-LLMs-dataset-perspective.html