Automating the drafting of judgment documents is pivotal to judicial efficiency, yet it remains challenging due to the dual requirements of comprehensive retrieval of legal information and rigorous logical reasoning. Existing approaches, typically relying on standard Retrieval-Augmented Generation and Supervised Fine-Tuning, often suffer from insufficient evidence recall, hallucinated statutory references, and logically flawed legal reasoning. To bridge this gap, we propose Judge-R1, a unified framework designed to enhance LLM-based judgment document generation by jointly improving legal information collection and judgment document generation. First, we introduce Agentic Legal Information Collection, which employs a dynamic planning agent to retrieve precise statutes and precedents from multiple sources. Second, we implement Rubric-Guided Optimization, a reinforcement learning phase utilizing Group Relative Policy Optimization (GRPO) with a comprehensive legal reward function to enforce adherence to judicial standards and reasoning logic. Extensive experiments on the JuDGE benchmark demonstrate that Judge-R1 significantly outperforms state-of-the-art baselines in both legal accuracy and generation quality.

Retrieval-augmented generation (RAG) has proven highly effective in improving large language models (LLMs) across various domains. However, there is no benchmark specifically designed to assess the effectiveness of RAG in the legal domain, which restricts progress in this area. To fill this gap, we propose LexRAG, the first benchmark to evaluate RAG systems for multi-turn legal consultations. LexRAG consists of 1,013 multi-turn dialogue samples and 17,228 candidate legal articles. Each sample is annotated by legal experts and consists of five rounds of progressive questioning. LexRAG includes two key tasks: (1) Conversational knowledge retrieval, requiring accurate retrieval of relevant legal articles based on multi-turn context. (2) Response generation, focusing on producing legally sound answers. To ensure reliable reproducibility, we develop LexiT, a legal RAG toolkit that provides a comprehensive implementation of RAG system components tailored for the legal domain. Additionally, we introduce an LLM-as-a-judge evaluation pipeline to enable detailed and effective assessment. Through experimental analysis of various LLMs and retrieval methods, we reveal the key limitations of existing RAG systems in handling legal consultation conversations. LexRAG establishes a new benchmark for the practical application of RAG systems in the legal domain, with its code and data available at https://github.com/CSHaitao/LexRAG.

This paper introduces JuDGE (Judgment Document Generation Evaluation), a novel benchmark for evaluating the performance of judgment document generation in the Chinese legal system. We define the task as generating a complete legal judgment document from the given factual description of the case. To facilitate this benchmark, we construct a comprehensive dataset consisting of factual descriptions from real legal cases, paired with their corresponding full judgment documents, which serve as the ground truth for evaluating the quality of generated documents. This dataset is further augmented by two external legal corpora that provide additional legal knowledge for the task: one comprising statutes and regulations, and the other consisting of a large collection of past judgment documents. In collaboration with legal professionals, we establish a comprehensive automated evaluation framework to assess the quality of generated judgment documents across various dimensions. We evaluate various baseline approaches, including few-shot in-context learning, fine-tuning, and a multi-source retrieval-augmented generation (RAG) approach, using both general and legal-domain LLMs. The experimental results demonstrate that, while RAG approaches can effectively improve performance in this task, there is still substantial room for further improvement. All the codes and datasets are available at: https://github.com/oneal2000/JuDGE

In legal case retrieval, existing work has shown that human-mediated conversational search can improve users’ search experience. One of the key problems for a practical conversational search system is how to ask high-quality clarifying questions to initiate conversations with users and understand their search intents. Previous works demonstrated that human-annotated external domain knowledge (such as event schemas) can improve the legal utility of clarifying questions generated by large language models. However, these methods are restricted to specific law systems or languages and cannot be generalized to others. To this end, we propose to generate context and domain-specific questions with LLMs without external annotations or knowledge by extracting information from top-retrieved documents given the current conversation context. Specifically, we construct a conversational legal case retrieval system CARQ that iteratively selects neighbor candidate case documents from the retrieved list at each conversation step to ask clarifying questions. We pretrain CARQ to capture the differences between legal cases and employ the reward augmented maximum likelihood to optimize the system directly for retrieval metrics. Extensive automated and human evaluations on three widely adopted legal case retrieval datasets demonstrate the superior effectiveness of our approach as compared with the state-of-the-art baselines.

Legal case retrieval is an important task in information retrieval that aims to retrieve relevant cases for given query cases. Conversational search paradigms have been shown to improve the search experience in legal case retrieval. However, there are two challenges in applying conversational search to legal scenarios. Firstly, legal search conversations often focus on different parts of legal case documents, but existing models struggle to capture the complex structural information and extract accurate relevance signals. Secondly, collecting large-scale conversational search datasets is costly, making it difficult to build reliable conversational legal case retrieval models. To address these challenges, we propose a Structure-Aware Matching Model (SAMM) for conversational legal case retrieval. SAMM extracts matching signals between conversational utterances and segments of the legal cases to incorporate structural information. We decouple the conversational search task into three subtasks and design pre-training tasks to overcome the lack of training data. Additionally, we create ConvLegal, the largest conversational legal case retrieval dataset to the best of our knowledge, for better evaluation of different methods. We train and evaluate SAMM and baselines on both a public dataset (CLCR) and ConvLegal. Experimental results demonstrate that SAMM outperforms existing models in legal case retrieval and conversational search.

Legal case retrieval aims to help legal workers find relevant cases related to their cases at hand, which is important for the guarantee of fairness and justice in legal judgments. While recent advances in neural retrieval methods have significantly improved the performance of open-domain retrieval tasks (e.g., Web search), their advantages haven’t been observed in legal case retrieval due to their thirst for annotated data. As annotating large-scale training data in legal domains is prohibitive due to the need for domain expertise, traditional search techniques based on lexical matching such as TF-IDF, BM25, and Query Likelihood are still prevalent in legal case retrieval systems. While previous studies have designed several pre-training methods for IR models in open-domain tasks, these methods are usually suboptimal in legal case retrieval because they cannot understand and capture the key knowledge and data structures in the legal corpus. To this end, we propose a novel pre-training framework named Caseformer that enables the pre-trained models to learn legal knowledge and domain-specific relevance-matching patterns in legal case retrieval without any human-labeled data. This framework is designed to support both dense retrieval models and neural re-ranking models. Through three unsupervised learning tasks, Caseformer is able to capture the special language, document structure, and relevance-matching patterns of legal case documents, making it a strong backbone for downstream legal case retrieval tasks. Experimental results show that our model has achieved state-of-the-art performance in both zero-shot and fine-tuning settings. Also, experiments on both Chinese and English legal datasets demonstrate that the effectiveness of Caseformer is language-independent in legal case retrieval.

Legal question answering based on case documents is a pivotal legal AI application and helps extract key elements from the legal case documents to promote downstream tasks. Intuitively, the form of this task is similar to legal machine reading comprehension. However, in existing legal machine reading comprehension datasets, the background information is much shorter than the legal case documents, and the questions are not designed from the perspective of legal knowledge. In this paper, we present LeDQA, the first Chinese legal case document-based question answering dataset to our best knowledge. Specifically, we build a comprehensive question schema (including 48 element-based questions) for the Chinese civil law by legal professionals. And considering the cost of human annotations are too expensive, we use one of the SOTA LLMs (i.e., GPT-4) to annotate the relevant sentences to these questions in each case document. The constructed dataset originates from Chinese civil cases and contains 100 case documents, 4,800 case-question pairs and 132,048 sentence-level relevance annotations. We implement several text matching algorithms for relevant sentence selection and various Large Language Models(LLMs) for legal question answering on LeDQA. The experimental results indicate that incorporating relevant sentences can benefit the performance of question answering models, but further efforts are still required to address the remaining challenges such as retrieving irrelevant sentences and incorrect reasoning between retrieved sentences.

As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling.

Legal case retrieval has received increasing attention in recent years. However, compared to ad hoc retrieval tasks, legal case retrieval has its unique challenges. First, case documents are rather lengthy and contain complex legal structures. Therefore, it is difficult for most existing dense retrieval models to encode an entire document and capture its inherent complex structure information. Most existing methods simply truncate part of the document content to meet the input length limit of PLMs, which will lead to information loss. Additionally, the definition of relevance in the legal domain differs from that in the general domain. Previous semantic-based or lexical-based methods fail to provide a comprehensive understanding of the relevance of legal cases. In this article, we propose a Structured Legal case Retrieval (SLR) framework, which incorporates internal and external structural information to address the above two challenges. Specifically, to avoid the truncation of long legal documents, the internal structural information, which is the organization pattern of legal documents, can be utilized to split a case document into segments. By dividing the document-level semantic matching task into segment-level subtasks, SLR can separately process segments using different methods based on the characteristic of each segment. In this way, the key elements of a case document can be highlighted without losing other content information. Second, toward a better understanding of relevance in the legal domain, we investigate the connections between criminal charges appearing in large-scale case corpus to generate a chargewise relation graph. Then, the similarity between criminal charges can be pre-computed as the external structural information to enhance the recognition of relevant cases. Finally, a learning-to-rank algorithm integrates the features collected from internal and external structures to output the final retrieval results. Experimental results on public legal case retrieval benchmarks demonstrate the superior effectiveness of SLR over existing state-of-the-art baselines, including traditional bag-of-words and neural-based methods. Furthermore, we conduct a case study to visualize how the proposed model focuses on key elements and improves retrieval performance.

Legal case retrieval is a special Information Retrieval (IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users’ information needs in legal case retrieval could be significantly different from those in Web search and traditional ad hoc retrieval tasks. While there are several studies that retrieve legal cases based on text similarity, the underlying search intents of legal retrieval users, as shown in this article, are more complicated than that yet mostly unexplored. To this end, we present a novel hierarchical intent taxonomy of legal case retrieval. It consists of five intent types categorized by three criteria, i.e., search for Particular Case(s) , Characterization , Penalty , Procedure , and Interest . The taxonomy was constructed transparently and evaluated extensively through interviews, editorial user studies, and query log analysis. Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval. Furthermore, we apply the proposed taxonomy to various downstream legal retrieval tasks, e.g., result ranking and satisfaction prediction, and demonstrate its effectiveness. Our work provides important insights into the understanding of user intents in legal case retrieval and potentially leads to better retrieval techniques in the legal domain, such as intent-aware ranking strategies and evaluation methodologies.

Legal case retrieval is a special IR task aiming to retrieve supporting cases for a given query case. Existing works have shown that conversational search paradigm can improve users' search experience in legal case retrieval. One of the keys to a practical conversational search system is how to ask high-quality clarifying questions to initiate conversations with users and understand their search intents. Recently, Large Language Models, such as ChatGPT and GPT-4, have shown superior ability in both open-domain QA and conversations with human. Thus it is natural to believe that they could be applied to legal conversational search as well. However, our preliminary study has shown that generating clarifying questions in legal conversational search with SOTA LLMs (e.g., GPT-4) often suffers from several problems such as duplication and low-utility contents. To address these problems, we propose LeClari, which leverages legal event schema as external knowledge to instruct LLMs to generate effective clarifying questions for legal conversational search. LeClari is constructed with a prompt module and a novel legal event selection module. The former defines a prompt with legal events for clarifying question generation and the latter selects potential event types by modeling the relationships of legal event types, conversational context, and candidate cases. We also propose ranking-oriented rewards and employ the reward augmented maximum likelihood (RAML) method to optimize LeClari directly based on the final retrieval performance of the conversational legal search system. Empirical results over two widely adopted legal case retrieval datasets demonstrate the effectiveness of our approach as compared with the state-of-the-art baselines.

Legal case retrieval, which aims to find relevant cases for a query case, plays a core role in the intelligent legal system. Despite the success that pre-training has achieved in ad-hoc retrieval tasks, effective pre-training strategies for legal case retrieval remain to be explored. Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. However, most existing language models have difficulty understanding the long-distance dependencies between different structures. Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements. Even subtle differences in key legal elements can significantly affect the judgement of relevance. However, existing pre-trained language models designed for general purposes have not been equipped to handle legal elements.

Legal case retrieval, which aims to retrieve relevant cases given a query case, has drawn increasing research attention in recent years. While much research has worked on developing automatic retrieval models, how to characterize relevance in this specialized information retrieval (IR) task is still an open question. Towards an in-depth understanding of relevance judgments, we conduct a laboratory user study that involves 72 participants of different domain expertise. In the user study, we collect the relevance score along with detailed explanations for the relevance judgment and various measures of the judgment process. From the collected data, we observe that both the subjective (e.g., domain expertise) and objective (e.g., query/case property) factors influence the relevance judgment process. By investigating the collected user explanations, we identify task-specific patterns of user attention distribution and re-think the criteria for relevance judgments. Moreover, we investigate the similarity in attention distribution between models and users. Further, we propose a two-stage framework that utilizes user attention to improve relevance estimation for legal case retrieval. Our study sheds light on understanding relevance judgments in legal case retrieval and provides implications for improving the design of corresponding retrieval systems.

The truncation of ranking lists predicted by retrieval models is vital to ensure users' search experience. Particularly, in specific vertical domains where documents are usually complicated and extensive (e.g., legal cases), the cost of browsing results is much higher than traditional IR tasks (e.g., Web search) and setting a reasonable cut-off position is quite necessary. While it is straightforward to apply existing result list truncation approaches to legal case retrieval, the effectiveness of these methods is limited because they only focus on simple document statistics and usually fail to capture the context information of documents in the ranking list. These existing efforts also treat result list truncation as an isolated task instead of a component in the entire ranking process, limiting the usage of truncation in practical systems. To tackle these limitations, we propose LeCut, a ranking list truncation model for legal case retrieval. LeCut utilizes contextual features of the retrieval task to capture the semantic-level similarity between documents and decides the best cut-off position with attention mechanisms. We further propose a Joint Optimization of Truncation and Reranking (JOTR) framework based on LeCut to improve the performance of truncation and retrieval tasks simultaneously. Comparison against competitive baselines on public benchmark datasets demonstrates the effectiveness of LeCut and JOTR. A case study is conducted to visualize the cut-off positions of LeCut and the process of how JOTR improves both retrieval and truncation tasks.

In recent years, legal case retrieval has attracted much attention in the IR research community. It aims to retrieve supporting cases for a given query case and contributes to better legal systems. While using a legal case retrieval system, users always feel difficult to construct accurate queries to express their information need, especially when they lack sufficient domain knowledge. Since conversational search has been widely recognized to fulfill users' complex and exploratory information need, we investigate whether conversational search paradigm can be adopted to improve users' legal case retrieval experience. We design a laboratory-based study to collect users' interaction behaviors and explicit feedback signals while using traditional and agent-mediated conversational legal case retrieval systems. Based on the collected data, we compare search behavior and outcome of these two different kinds of interaction paradigms. Compared with the traditional one, experimental results show that users can achieve better retrieval performance with the conversational case retrieval system. Moreover, conversational system can also save users' efforts in formulating queries and examining results.

Legal case retrieval is of vital importance for ensuring justice in different kinds of law systems and has recently received increasing attention in information retrieval (IR) research. However, the relevance judgment criteria of previous retrieval datasets are either not applicable to non-cited relationship cases or not instructive enough for future datasets to follow. Besides, most existing benchmark datasets do not focus on the selection of queries. In this paper, we construct the Chinese Legal Case Retrieval Dataset (LeCaRD), which contains 107 query cases and over 43,000 candidate cases. Queries and results are adopted from criminal cases published by the Supreme People's Court of China. In particular, to address the difficulty in relevance definition, we propose a series of relevance judgment criteria designed by our legal team and corresponding candidate case annotations are conducted by legal experts. Also, we develop a novel query sampling strategy that takes both query difficulty and diversity into consideration. For dataset evaluation, we implemented several existing retrieval models on LeCaRD as baselines. The dataset is now available to the public together with the complete data processing details.

Legal case retrieval is a specialized IR task aiming to retrieve supporting cases given a query case. While recent research efforts are committed to improving the automatic retrieval models' performances, little attention has been paid to the practical search interactions between users and systems in this task. Therefore, we focus on investigating user behavior in the scenario of legal case retrieval. Specifically, we conducted a laboratory user study that involved 45 participants majoring in law to collect users' rich interactions and relevance assessments. With the collected data, we first analyzed the characteristics of the search process in legal case retrieval practice. We observed significant differences between legal case retrieval and general web search in various search behavior. These differences highlight the necessity of in-depth investigating user behavior in legal case retrieval and re-thinking the application of related mechanisms developed based on the user models in Web search. Then we investigated factors that would influence search behavior from different perspectives, including task difficulty and domain expertise. Finally, we shed light on implicit feedback in legal case retrieval and designed a predictive model for relevance based on user behavior. Our work provides a better understanding of user interactions in the legal case retrieval process, which can benefit the design of the corresponding retrieval systems to support legal practitioners.