Selection of LLM Fine-Tuning Data based on Orthogonal Rules

Understand

High-quality training data is critical to the performance of large language models (LLMs).

• Recent work has explored using LLMs to rate and select data based on a small set of human-designed criteria (rules), but these approaches often rely heavily on heuristics, lack principled metrics for rule evaluation, and generalize poorly to new tasks.
• We propose a novel rule-based data selection framework that introduces a metric based on the orthogonality of rule score vectors to evaluate and select complementary rules.
• Our automated pipeline first uses LLMs to generate diverse rules covering multiple aspects of data quality, then rates samples according to these rules and applies the determinantal point process (DPP) to select the most independent rules.