用少量样本预测微调所需数据量，避免盲目标注。
Efficiently Estimating Data Efficiency for Language Model Fine-tuning
- 通过低置信度样本的梯度相似性预测数据效率。
- 在30个任务上预测误差仅8.6%，减少数百次无效标注。
- 适合需要高效微调的NLP研究者和工程师。
尽管大语言模型在众多下游任务中具备一定的零样本能力，但微调仍是提升性能的常用手段。然而，任务的数据效率——即达到目标性能所需的微调样本数——通常未知，导致频繁的增量标注与重训练，成本高昂。我们通过对30个专业任务的分析发现，表现良好的大模型可能在零样本下表现不佳，但经微调后可显著提升性能。为此，我们提出一种量化数据效率的指标，并基于少量标注样本，利用低置信度样本的梯度余弦相似性来预测数据效率。在多样化任务上的实验表明，该方法整体预测误差为8.6%，通常可避免每项任务数百次不必要的标注。实验结果与代码已开源。
原文摘要 · Abstract (English)
While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However, a task's data efficiency--i.e., the number of fine-tuning examples needed to achieve a desired level of performance--is often unknown, resulting in costly cycles of incremental annotation and retraining. Indeed, we demonstrate across a curated set of 30 specialized tasks that performant LLMs may struggle zero-shot but can attain stronger performance after fine-tuning. This motivates the need for methods to predict a task's data efficiency without requiring incremental annotation. After introducing a concrete metric that quantifies a task's data efficiency, we propose using the gradient cosine similarity of low-confidence examples to predict data efficiency based on a small number of labeled samples. We validate our approach on a diverse set of tasks with varying data efficiencies, attaining 8.6% error in overall data efficiency prediction and typically eliminating hundreds of unnecessary annotations on each task. Our experiment results and implementation code are available on GitHub.
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