IterSelectTune: An Iterative Training Framework for Efficient Instruction-Tuning Data Selection
This addresses the challenge of reducing computational costs and human involvement in instruction tuning for LLM developers, though it is incremental as it builds on existing data selection methods.
The paper tackles the problem of selecting high-quality instruction-tuning data for large language models without human effort, and the result is that their method, using only 20% of the source data, outperforms models trained on the full dataset across multiple benchmarks.
As large language models (LLMs) continue to advance, instruction tuning has become critical for improving their ability to generate accurate and contextually appropriate responses. Although numerous instruction-tuning datasets have been developed to enhance LLM performance, selecting high-quality instruction data from large source datasets typically demands significant human effort. In this work, we introduce $\textbf{IterSelectTune}$, an efficient, cost-effective iterative training policy for selecting high-quality instruction data with no human involvement and limited reliance on GPT-4. By fine-tuning on approximately 20\% of the source data, our method consistently outperforms models fine-tuned on the full dataset across multiple benchmarks and public test datasets. These results highlight the effectiveness of our approach in enhancing LLM performance while reducing the computational resources required for instruction tuning.