Chang Yue

Selection of LLM Fine-Tuning Data Based on Orthogonal Rules

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 eval