DELIFT: Data Efficient Language model Instruction Fine Tuning
Authors: name: Ishika Agarwal, name: Krishna Killamsetty, name: Lucian Popa, name: Marina Danilevksy

Fine-tuning large language models (LLMs) is essential for enhancing their
performance on specific tasks but is often resource-intensive due to redundant
or uninformative data. To address this inefficiency, we introduce DELIFT (Data
Efficient Language model Instruction Fine-Tuning), a novel algorithm that
systematically optimizes data selection across the three key stages of
fine-tuning: (1) instruction tuning, (2) task-specific fine-tuning (e.g.,
reasoning, question-answering), and (3) continual fine-tuning (e.g.,
incorporating new data versions). Unlike existing methods that focus on
single-stage optimization or rely on computationally intensive gradient
calculations, DELIFT operates efficiently across all stages. Central to our
approach is a pairwise utility metric that quantifies how beneficial a data
sample is for improving the model's responses to other samples, effectively
measuring the informational value relative to the model's current capabilities.
By leveraging different submodular functions applied to this metric, DELIFT
selects diverse and optimal subsets that are useful across all stages of
fine-tuning. Experiments across various tasks and model scales demonstrate that
DELIFT can reduce the fine-tuning data size by up to 70% without compromising
performance, offering significant computational savings and outperforming
existing methods in both efficiency and efficacy.