Lightweight Adapted Retrieval-Augmented Inference Network for Legal Issues

Abstract

Legal text generation and legal question-and-answer tasks impose stringent requirements on factual accuracy, evidence traceability and normative consistency. Conventional dynamic retrieval-augmented generation methods are difficult to directly adapt to legal task demands such as legal provision citation, terminology standardization and case evidence organization. Following the DRAGIN paradigm, this paper proposes LARIN (Lightweight Adapted Retrieval-Augmented Inference Network for Legal Issues), an adaptive retrieval-augmented reasoning framework tailored for legal scenarios. While maintaining the mainstream dynamic retrieval workflow, LARIN makes targeted optimizations in three key modules: retrieval triggering, query construction and evidence fusion. Specifically, LINDA identifies retrieval trigger points by comprehensively considering uncertainty, attention influence and semantic importance; JUDGE rewrites queries for legal terms and statutory expressions; MERF conducts relevance ranking, redundancy elimination and evidence fusion for legal provisions and case materials. In the CAIL2018 Chinese legal judgment prediction task, LARIN achieves a charge prediction exact-match accuracy of 0.3316, a micro-F1 score of 0.3837, and a precision of 0.3951, while the sentencing exact-match accuracy reaches 0.1575. Meanwhile, its average retrieval frequency is 1.81 and average token consumption stands at 373.15. Experimental results on CAIL2018 demonstrate that legal-oriented adaptation based on DRAGIN improves retrieval-augmented reasoning in this Chinese legal judgment prediction setting while maintaining low retrieval overhead. Cross-dataset robustness on additional legal QA or judgment benchmarks remains to be further validated.