ASP2LJ combines synthetic case generation with adversarial self-play for lawyer agents, improving legal judgment prediction on a Chinese benchmark and on a new rare-case dataset.
A Cross-Lingual Statutory Article Retrieval Dataset for Taiwan Legal Studies
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abstract
This paper introduces a cross-lingual statutory article retrieval (SAR) dataset designed to enhance legal information retrieval in multilingual settings. Our dataset features spoken-language-style legal inquiries in English, paired with corresponding Chinese versions and relevant statutes, covering all Taiwanese civil, criminal, and administrative laws. This dataset aims to improve access to legal information for non-native speakers, particularly for foreign nationals in Taiwan. We propose several LLM-based methods as baselines for evaluating retrieval effectiveness, focusing on mitigating translation errors and improving cross-lingual retrieval performance. Our work provides a valuable resource for developing inclusive legal information retrieval systems.
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ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework
ASP2LJ combines synthetic case generation with adversarial self-play for lawyer agents, improving legal judgment prediction on a Chinese benchmark and on a new rare-case dataset.