用智能采样从合成数据中挑出更少但更有效的训练样本
Less is More: Adaptive Coverage for Synthetic Training Data
- 基于最大覆盖问题设计采样算法，选出最具代表性的子集
- 用采样后数据训练模型，准确率高于全量合成数据
- 适合急需快速部署的场景，如新网络舆情分类
利用大型语言模型（如 Google's Gemma 和 OpenAI's GPT）生成合成训练数据，为构建大规模标注数据集提供可行方案。在需要快速部署模型的场景下，例如识别新兴社交媒体趋势或应对与当前事件相关的新型网络滥用行为，合成数据生成能力极具价值。尽管已有研究探讨了合成数据与人工标注数据的可比性，本文提出一种基于最大覆盖问题的新采样算法，从合成数据集中选择代表性子集。实验表明，使用该上下文感知采样子集训练分类器，性能优于使用全部合成数据的训练方式。这一‘少即是多’的方法不仅提升模型准确率，还显著减少所需数据量，从而实现更高效的模型微调。
原文摘要 · Abstract (English)
Synthetic training data generation with Large Language Models (LLMs) like Google's Gemma and OpenAI's GPT offer a promising solution to the challenge of obtaining large, labeled datasets for training classifiers. When rapid model deployment is critical, such as in classifying emerging social media trends or combating new forms of online abuse tied to current events, the ability to generate training data is invaluable. While prior research has examined the comparability of synthetic data to human-labeled data, this study introduces a novel sampling algorithm, based on the maximum coverage problem, to select a representative subset from a synthetically generated dataset. Our results demonstrate that training a classifier on this contextually sampled subset achieves superior performance compared to training on the entire dataset. This "less is more" approach not only improves model accuracy but also reduces the volume of data required, leading to potentially more efficient model fine-tuning.
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