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

Understand

In the realm of Large Language Models (LLMs), the balance between instruction data quality and quantity is a focal point.

• Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from 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 metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
• Through the application of IFD, cherry samples can be pinpointed, leading to a marked uptick in model training efficiency.