From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

arXiv:2308.12032v4 Announce Type: replace
Abstract: In the realm of Large Language Models, the balance between instruction data quality and quantity has become a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from vast open-source datasets, effectively minimizing manual curation and potential cost for instruction tuning an LLM. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal tool to identify discrepancies between a model's expected responses and its …

arxiv boosting cs.cl data llm llm performance performance quality type