Abstract
We propose ARISE, a framework that iteratively induces rules and generates synthetic data for text classification. We combine synthetic data generation and automatic rule induction, via bootstrapping, to iteratively filter the generated rules and data. We induce rules via inductive generalisation of syntactic n-grams, enabling us to capture a complementary source of supervision. These rules alone lead to performance gains in both, in-context learning (ICL) and fine-tuning (FT) settings. Similarly, use of augmented data from ARISE alone improves the performance for a model, outperforming configurations that rely on complex methods like contrastive learning. Further, our extensive experiments on various datasets covering three full-shot, eight few-shot and seven multilingual variant settings demonstrate that the rules and data we generate lead to performance improvements across these diverse domains and languages.
Abstract (translated)
我们提出了ARISE框架，该框架通过迭代地诱导规则并生成合成数据来实现文本分类。我们结合了合成数据生成和自动规则归纳，并通过引导式方法迭代地过滤生成的规则和数据。我们利用语法n-gram的归纳概括来诱导规则，这使我们能够捕捉到一种补充性的监督来源。仅凭这些规则，在上下文学习（ICL）和微调（FT）设置中都能实现性能提升。同样，单独使用ARISE产生的增强数据也能提高模型的性能，并且优于依赖复杂方法如对比学习等配置。 此外，我们在涵盖三个全样本、八个少量样本以及七个多语言变体设置的各种数据集上进行了广泛的实验，结果表明我们生成的规则和数据能够跨这些多样化的领域和语言实现性能提升。
URL
https://arxiv.org/abs/2502.05923
