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

By Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao. North American Chapter of the Association for Computational Linguistics, 2023

373 citations recorded by the source corpus.

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. Empirical validations on datasets like Alpaca and WizardLM underpin our findings; with a mere 10% of original data input, our strategy showcases improved results. This synthesis of self-guided cherry-picking and the IFD metric signifies a transformative leap in the instruction tuning of LLMs, promising both efficiency and resource-conscious advancements. Codes, data, and models are available.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/e3052ebca5eeae6a8a73e44517903d39746f5f3a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

  1. Keqing He
  2. Weihao Zeng
  3. Wei Liu
  4. Yong Jiang
  5. Junxian He

Citations in the corpus