Token-Efficient Leverage Learning in Large Language Models
This addresses data scarcity issues for practitioners using LLMs in resource-constrained settings, though it appears incremental as an optimization of existing fine-tuning methods.
The paper tackles the challenge of adapting large language models to low-resource scenarios by introducing Leverage Learning, specifically Token-Efficient Leverage Learning (TELL), which reduces task data requirements by up to nearly an order of magnitude compared to supervised fine-tuning while delivering competitive performance.
Large Language Models (LLMs) have excelled in various tasks but perform better in high-resource scenarios, which presents challenges in low-resource scenarios. Data scarcity and the inherent difficulty of adapting LLMs to specific tasks compound the challenge. To address the twin hurdles, we introduce \textbf{Leverage Learning}. We present a streamlined implement of this methodology called Token-Efficient Leverage Learning (TELL). TELL showcases the potential of Leverage Learning, demonstrating effectiveness across various LLMs and low-resource tasks, ranging from $10^4$ to $10^6$ tokens. It reduces task data requirements by up to nearly an order of magnitude compared to conventional Supervised Fine-Tuning (SFT) while delivering competitive performance. With the same amount of task data, TELL leads in improving task performance compared to SFT. We discuss the mechanism of Leverage Learning, suggesting it aligns with quantization hypothesis and explore its promising potential through empirical testing.