Improving Data Efficiency via Curating LLM-Driven Rating Systems
Keywords: Data Selection, LLM, Instruction Tuning
TL;DR: We systematically analyze the inherent errors of the rated scores generated by advanced LLMs, which are typically used for data selection, and then provide a score curation method to rectify and ensure the quality of LLM rated scores.
Abstract: Instruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outperform larger datasets, challenging traditional data scaling laws. While LLM-based data quality rating systems offer a cost-effective alternative to human annotation, they often suffer from inaccuracies and biases, even in powerful models like GPT-4. In this work, we introduce $DS^2$, a **D**iversity-aware **S**core curation method for **D**ata **S**election. By systematically modeling error patterns through a score transition matrix, $DS^2$ corrects LLM-based scores and promotes diversity in the selected data samples. Our approach shows that a curated subset (just 3.3\% of the original dataset) outperforms full-scale datasets (300k samples) across various machine-alignment benchmarks, and matches or surpasses human-aligned datasets such as LIMA with the same sample size (1k samples). These findings challenge conventional data scaling assumptions, highlighting that redundant, low-quality samples can degrade performance and reaffirming that ``more can be less''.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 2467
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