对比多种选样策略，发现随机选择仍是文本增强的可靠默认方案
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification
- 比较少样本学习中的多种选样策略在文本增强中的效果
- 多数有意识选样仅带来微弱性能提升，尤其对分布外数据
- 除非新方法突破，否则随机选样仍是最稳妥实践
生成式大语言模型（LLMs）越来越多用于数据增强任务，通过改写或生成新文本并用于分类器微调。现有研究多采用少样本场景，将样本作为提示输入给LLM以生成更优增强数据，但样本通常随机选取，缺乏对更‘明智’选样策略的系统评估。本文对比了少样本学习中已有的选样策略在基于LLM的文本增强中的表现，并评估其在分布内与分布外数据上的分类器性能。结果表明，尽管某些有意识选样策略能略微提升模型性能，尤其是分布外数据，但这种情况很少见且增益微弱。除非有进一步技术突破，随机选样仍是增强实践中的合理默认选项。
原文摘要 · Abstract (English)
The generative large language models (LLMs) are increasingly used for data augmentation tasks, where text samples are paraphrased (or generated anew) and then used for classifier fine-tuning. Existing works on augmentation leverage the few-shot scenarios, where samples are given to LLMs as part of prompts, leading to better augmentations. Yet, the samples are mostly selected randomly and a comprehensive overview of the effects of other (more ``informed'') sample selection strategies is lacking. In this work, we compare sample selection strategies existing in few-shot learning literature and investigate their effects in LLM-based textual augmentation. We evaluate this on in-distribution and out-of-distribution classifier performance. Results indicate, that while some ``informed'' selection strategies increase the performance of models, especially for out-of-distribution data, it happens only seldom and with marginal performance increases. Unless further advances are made, a default of random sample selection remains a good option for augmentation practitioners.
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