The core idea of leverage learning is that a task often consists of task-specific and non-specific capabilities. Traditionally, massive datasets are used to fine-tune both these capabilities, which could lead to inefficient use of valuable training data. Leverage learning suggests that it is possible to strategically use minimal task-specific data to enhance task-specific capabilities, while non-specific capabilities can be learned from more general data.
The paper introduce and detail a minimalistic implementation of leverage learning, named Token-Efficient Leverage Learning (TELL). In low-resource settings, TELL can activate tasks that are unfeasible with conventional methods; for tasks that are feasible, TELL achieves significantly better outcomes with the same amount of data. If performance parity is the goal, TELL drastically reduces the data required compared to traditional methods, often by nearly 10x less, achieving similar or superior results. A notable example is that just 10 domain-specific data entries can substantially enhance LLM performance in that domain.
It's important to note that the traditional methods referenced here generally include LoRA, which is a top-tier baseline in Parameter-Efficient Fine-Tuning (PEFT) and has been proven to outperform full-parameter fine-tuning in low-resource settings. This work potentially makes various applications of fine-tuning in low-resource scenarios (such as user-customized models) more feasible.
The paper introduce and detail a minimalistic implementation of leverage learning, named Token-Efficient Leverage Learning (TELL). In low-resource settings, TELL can activate tasks that are unfeasible with conventional methods; for tasks that are feasible, TELL achieves significantly better outcomes with the same amount of data. If performance parity is the goal, TELL drastically reduces the data required compared to traditional methods, often by nearly 10x less, achieving similar or superior results. A notable example is that just 10 domain-specific data entries can substantially enhance LLM performance in that domain.
It's important to note that the traditional methods referenced here generally include LoRA, which is a top-tier baseline in Parameter-Efficient Fine-Tuning (PEFT) and has been proven to outperform full-parameter fine-tuning in low-resource settings. This work potentially makes various applications of fine-tuning in low-resource scenarios (such as user-customized models) more feasible.