Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities

Understand

Selecting appropriate training data is crucial for instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve balanced performance across different tasks.

• Influence-based methods show promise in achieving (1), by estimating the contribution of each training example to the model's predictions, but often struggle with (2).
• Our systematic investigation reveals that this underperformance can be attributed to an inherent bias, where some tasks intrinsically have greater influence than others.
• As a result, data selection is often biased towards these tasks, not only hurting the model's performance on others but also, counterintuitively, harming performance on these high-influence tasks themselves.