Data scarcity remains a persistent challenge in low-resource domains. While existing data augmentation methods leverage the generative capabilities of large language models (LLMs) to produce large volumes of synthetic data, these approaches often prioritize quantity over quality and lack domain-specific strategies. In this work, we introduce DALDALL, a persona-based data augmentation framework tailored for legal information retrieval (IR). Our method employs domain-specific professional personas--such as attorneys, prosecutors, and judges--to generate synthetic queries that exhibit substantially greater lexical and semantic diversity than vanilla prompting approaches. Experiments on the CLERC and COLIEE benchmarks demonstrate that persona-based augmentation achieves improvement in lexical diversity as measured by Self-BLEU scores, while preserving semantic fidelity to the original queries. Furthermore, dense retrievers fine-tuned on persona-augmented data consistently achieve competitive or superior recall performance compared to those trained on original data or generic augmentations. These findings establish persona-based prompting as an effective strategy for generating high-quality training data in specialized, low-resource domains.

翻译：数据稀缺仍是低资源领域中持续存在的挑战。虽然现有数据增强方法利用大型语言模型的生成能力产生大量合成数据，但这些方法往往优先考虑数量而非质量，且缺乏领域特定策略。本研究提出DALDALL，一种面向法律信息检索的基于人格的数据增强框架。该方法采用领域专业人格——如律师、检察官和法官——来生成合成查询，相较于普通提示方法，这些查询展现出显著更高的词汇与语义多样性。在CLERC和COLIEE基准上的实验表明，基于人格的数据增强在通过自我BLEU分数衡量的词汇多样性方面取得了改进，同时保持了对原始查询的语义保真度。此外，在人格增强数据上微调的密集检索器在召回性能上相比基于原始数据或通用增强数据训练的检索器始终表现出竞争力或更优。这些发现确立了基于人格的提示作为在专业低资源领域生成高质量训练数据的有效策略。