Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model
  Collaboration
Yiquan Wu Siying Zhou Yifei Liu Weiming Lu Xiaozhong Liu Yating Zhang Changlong Sun Fei Wu Kun Kuang
Legal Judgment Prediction (LJP) has become an increasingly crucial task in
Legal AI, i.e., predicting the judgment of the case in terms of case fact
description. Precedents are the previous legal cases with similar facts, which
are the basis for the judgment of the subsequent case in national legal
systems. Thus, it is worthwhile to explore the utilization of precedents in the
LJP. Recent advances in deep learning have enabled a variety of techniques to
be used to solve the LJP task. These can be broken down into two categories:
large language models (LLMs) and domain-specific models. LLMs are capable of
interpreting and generating complex natural language, while domain models are
efficient in learning task-specific information. In this paper, we propose the
precedent-enhanced LJP framework (PLJP), a system that leverages the strength
of both LLM and domain models in the context of precedents. Specifically, the
domain models are designed to provide candidate labels and find the proper
precedents efficiently, and the large models will make the final prediction
with an in-context precedents comprehension. Experiments on the real-world
dataset demonstrate the effectiveness of our PLJP. Moreover, our work shows a
promising direction for LLM and domain-model collaboration that can be
generalized to other vertical domains.