DALDALL: Data Augmentation for Lexical and Semantic Diverse in Legal Domain by leveraging LLM-Persona

2026-03-23

AI Summary

This work addresses the challenge of data scarcity in legal information retrieval, where existing data augmentation techniques struggle to produce high-quality, domain-appropriate queries. To overcome this limitation, the authors propose a novel large language model–based data augmentation framework that incorporates professional role–specific prompts—such as those emulating lawyers, judges, and prosecutors—into the generation process. This approach, which uniquely integrates domain-expert personas into prompting strategies, significantly enhances both lexical diversity and semantic fidelity of the synthesized queries. Experimental results on the CLERC and COLIEE benchmarks demonstrate that the generated queries achieve lower Self-BLEU scores, indicating higher diversity, and when used to fine-tune dense retrievers, yield superior recall performance compared to current state-of-the-art baselines.

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

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.

Problem

• data augmentation
• legal domain
• lexical diversity
• semantic fidelity
• low-resource domains

Innovation

• persona-based prompting
• legal information retrieval
• data augmentation
• lexical diversity
• large language models