With the development of legal intelligence, Criminal Court View Generation
has attracted much attention as a crucial task of legal intelligence, which
aims to generate concise and coherent texts that summarize case facts and
provide explanations for verdicts. Existing researches explore the key
information in case facts to yield the court views. Most of them employ a
coarse-grained approach that partitions the facts into broad segments (e.g.,
verdict-related sentences) to make predictions. However, this approach fails to
capture the complex details present in the case facts, such as various criminal
elements and legal events. To this end, in this paper, we propose an Event
Grounded Generation (EGG) method for criminal court view generation with
cooperative (Large) Language Models, which introduces the fine-grained event
information into the generation. Specifically, we first design a LLMs-based
extraction method that can extract events in case facts without massive
annotated events. Then, we incorporate the extracted events into court view
generation by merging case facts and events. Besides, considering the
computational burden posed by the use of LLMs in the extraction phase of EGG,
we propose a LLMs-free EGG method that can eliminate the requirement for event
extraction using LLMs in the inference phase. Extensive experimental results on
a real-world dataset clearly validate the effectiveness of our proposed method.

通过合作的（大型）语言模型，我们提出了一种基于事件的刑事法庭观点生成方法（EGG），将细粒度的事件信息引入生成过程中，实验证明了我们所提出方法的有效性。

事件驱动的刑事法庭观点生成与合作 (大型) 语言模型

Event Grounded Criminal Court View Generation withCooperative (Large)  Language Models

Large Language Models (LLMs) have gained prominence in the field of Legal
Intelligence, offering potential applications in assisting legal professionals
and laymen. However, the centralized training of these Legal LLMs raises data
privacy concerns, as legal data is distributed among various institutions
containing sensitive individual information. This paper addresses this
challenge by exploring the integration of Legal LLMs with Federated Learning
(FL) methodologies. By employing FL, Legal LLMs can be fine-tuned locally on
devices or clients, and their parameters are aggregated and distributed on a
central server, ensuring data privacy without directly sharing raw data.
However, computation and communication overheads hinder the full fine-tuning of
LLMs under the FL setting. Moreover, the distribution shift of legal data
reduces the effectiveness of FL methods. To this end, in this paper, we propose
the first Federated Legal Large Language Model (FedJudge) framework, which
fine-tunes Legal LLMs efficiently and effectively. Specifically, FedJudge
utilizes parameter-efficient fine-tuning methods to update only a few
additional parameters during the FL training. Besides, we explore the continual
learning methods to preserve the global model's important parameters when
training local clients to mitigate the problem of data shifts. Extensive
experimental results on three real-world datasets clearly validate the
effectiveness of FedJudge. Code is released at
this https URL

通过联邦学习方法，本文提出了第一个高效、有效的联邦法律大型语言模型（FedJudge）框架，用于在保护数据隐私的前提下，实现对法律大型语言模型的本地调优。

FedJudge: 联合法律大型语言模型

FedJudge: Federated Legal Large Language Model

Artificial intelligence researchers have made significant advances in legal
intelligence in recent years. However, the existing studies have not focused on
the important value embedded in judgments reversals, which limits the
improvement of the efficiency of legal intelligence. In this paper, we propose
a causal Framework for Accurately Inferring case Reversals (FAIR), which models
the problem of judgments reversals based on real Chinese judgments. We mine the
causes of judgments reversals by causal inference methods and inject the
obtained causal relationships into the neural network as a priori knowledge.
And then, our framework is validated on a challenging dataset as a legal
judgment prediction task. The experimental results show that our framework can
tap the most critical factors in judgments reversal, and the obtained causal
relationships can effectively improve the neural network's performance. In
addition, we discuss the generalization ability of large language models for
legal intelligence tasks using ChatGPT as an example. Our experiment has found
that the generalization ability of large language models still has defects, and
mining causal relationships can effectively improve the accuracy and explain
ability of model predictions.

本文提出了 FAIR 因果框架，通过因果推理方法挖掘判决翻转的原因，并将得到的因果关系作为先验知识注入到神经网络中，从而改善了神经网络的性能和解释能力，并通过 ChatGPT 作为示例讨论了大型语言模型在法律智能任务中的泛化能力。