DELIFT: Data Efficient Language model Instruction Fine Tuning
Ishika Agarwal, Krishna Killamsetty, Lucian Popa, Marina Danilevksy
2024-11-11
Summary
This paper introduces DELIFT, a new method for efficiently fine-tuning large language models (LLMs) to improve their performance on specific tasks while using less data.
What's the problem?
Fine-tuning LLMs is important for making them better at specific jobs, but it usually requires a lot of data and computational resources. Many existing methods use too much data that isn’t always helpful, leading to wasted time and effort. This makes it hard for developers to optimize LLMs for specialized tasks without spending a lot of money and resources.
What's the solution?
DELIFT addresses this issue by optimizing how data is selected during the fine-tuning process. It focuses on three stages: instruction tuning (teaching the model to follow instructions), task-specific fine-tuning (improving skills for specific tasks), and continual fine-tuning (updating the model with new information). DELIFT uses a unique metric to evaluate the usefulness of each data sample, allowing it to choose only the most beneficial data. This results in a reduction of up to 70% in the amount of data needed for fine-tuning while still maintaining high performance.
Why it matters?
This research is significant because it makes fine-tuning large language models more accessible and efficient. By reducing the amount of data required, DELIFT allows developers to improve their models without needing extensive resources, which can lead to better AI applications in various fields like customer service, education, and content creation.
Abstract
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.