Dual-system theory distinguishes between fast, intuitive System 1 and slow, deliberative System 2. While this dichotomy describes many forms of reasoning, it oversimplifies the reality of expert legal reasoning. Legal reasoning is not merely a process of slow, logical deliberation. It is intrinsically normative, embedding precedent analysis, statutory interpretation, policy balancing, and social values. This paper envisions a reasoning architecture for legal reasoning, System L (Legal System 2), which extends traditional System 2 by integrating domain-specific normative frameworks in a structured manner. Using the IRAC (Issue–Rule–Application–Conclusion) structure as a backbone model, System L represents a blueprint for the next generation of cognitive and AI systems capable of human-like legal reasoning.

Automatic legal judgment prediction (LJP) has recently received increasing attention in the natural language processing community because of its practical values in the real world.Significant progress has been achieved on LJP in the past decade.However, most existing LJP research primarily focuses on developing methods that achieve better performance on standard evaluation datasets, with limited emphasis on the long-term advancement of the field beyond improving evaluation metrics.In this position paper, we reflect on the state of the art in LJP research, and explore issues that should motivate researchers to think beyond merely enhancing performance metrics, with the ultimate goal of sparking discussions among LJP researchers about the future trajectory of the field.

Legal Judgment Prediction (LJP) focuses on predicting judgment results based on the facts of cases. While State-of-the-Art (SOTA) methods have shown impressive performance in law article prediction and charge prediction, they still exhibit weaknesses in prison term prediction. One major reason is that existing models fail to mimic human legal quantitative reasoning to understand monetary features in case facts. Consequently, they do not rigorously quantify the severity of the crime, which is essential for prison term prediction. In this article, we explore and explain how to leverage monetary features to improve LJP via quantitative reasoning. Specifically, we propose QR-LJP, a quantitative reasoning-based LJP model, to integrate legal reasoning knowledge into the prediction process. QR-LJP first employs a curated LLM to extract monetary values from case facts and uses legal quantitative reasoning logic to determine the total crime amount, serving as the quantitative measure of the crime’s severity. This measure is subsequently used to make judgment predictions. We evaluate our model on the real-world dataset CAIL-2018. Experimental results demonstrate that our model outperforms current SOTAs, highlighting the effectiveness of legal quantitative reasoning. Moreover, applying our quantitative reasoning strategy to existing SOTA methods yields significant improvements, especially in macro-F1 scores.

In an era where mobile devices are ubiquitous, digital distribution platforms such as the Google Play Store have become integral to our daily lives, hosting millions of applications and serving billions of users. Users can leave reviews to provide developers with valuable feedback, including requests for new features and reports of issues. These user reviews play a crucial role in software development, testing, and maintenance by informing developers about user needs and potential problems, which motivates us to revisit a key problem: given user reviews, how can we automatically identify the relevant code snippets from software codebases to assist developers in addressing the reviews? Existing practices to address this problem typically involve calculating the similarity between user reviews and code snippets. However, we identify three key limitations. First, although existing methods show promising results on individual projects, their high performance cannot be generalized across projects. Second, the state-of-the-art approach models the problem as a one-to-one relationship between a user review and code snippets, ignoring the one-to-many relationship that often exists. Third, the state-of-the-art approach focuses solely on the direct relationship between reviews and code snippets, overlooking the interconnections among code snippets themselves, which contain valuable information that can aid in accurately identifying relevant code. To address these limitations and advance the state of the art, we propose YourCoLo , a novel approach that fully leverages contextual information, one-to-many relationships, and inter-code connections. Specifically, YourCoLo is powered by three novel designs: (1) a prompt-enhanced mechanism to incorporate rich project-level context into code localization, (2) a new loss function designed to handle the one-to-many relationships between user reviews and multiple relevant code snippets, and (3) a ranking strategy that considers interconnections among related code snippets. Our experimental evaluation shows that YourCoLo substantially outperforms state-of-the-art models, surpassing CodeBERT, CodeLlama, and GraphCodeBERT by 18.3, 9.3, and 7.7 percentage points at the method level and by 18.4, 7.7, and 7.0 percentage points at the file level (in terms of mean reciprocal rank). In addition, YourCoLo also achieves improvements of 8.8 percentage points and 6.8 percentage points in mean average precision (MAP) at the method and file levels, respectively, compared to the state-of-the-art method. These results underscore YourCoLo ’s effectiveness and its potential to guide developers more accurately toward the code snippets most pertinent to user feedback.

While AI systems are capable of reading texts and seeing images, they typically perceive surface information explicitly conveyed with limited abilities to comprehend hidden messages (e.g., a double-edged remark). We propose the novel task of advertisement understanding: given an advertisement, which can be a text, an image, or a video, the goal is to identify the persuasion strategies used and determine the (possibly hidden) messages conveyed. Efforts on this task could enhance machine comprehension capabilities, and provide users with increased situation awareness w.r.t. the advertised message and thus possibly enable mindful decision making. We believe that this task presents long-term challenges to AI researchers and that successful understanding of ads could bring machine understanding one important step closer to human understanding.

Multimodal neural machine translation (MNMT) has received increasing attention due to its widespread applications in various fields such as cross-border e-commerce and cross-border social media platforms.The task aims to integrate other modalities, such as the visual modality, with textual data to enhance translation performance.We survey the major milestones in MNMT research, providing a comprehensive overview of relevant datasets and recent methodologies, and discussing key challenges and promising research directions.

Legal Judgment Prediction (LJP) seeks to predict case outcomes given available case information, offering practical value for both legal professionals and laypersons. However, a key limitation of existing LJP models is their limited adaptability to statutory revisions. Current SOTA models are neither designed nor evaluated for statutory revisions. To bridge this gap, we introduce LawShift, a benchmark dataset for evaluating LJP under statutory revisions. Covering 31 fine-grained change types, LawShift enables systematic assessment of SOTA models' ability to handle legal changes. We evaluate five representative SOTA models on LawShift, uncovering significant limitations in their response to legal updates. Our findings show that model architecture plays a critical role in adaptability, offering actionable insights and guiding future research on LJP in dynamic legal contexts.

Recent years have seen increasing attention on Legal Case Retrieval (LCR), a key task in the area of Legal AI that concerns the retrieval of cases from a large legal database of historical cases that are similar to a given query.This paper presents a survey of the major milestones made in LCR research, targeting researchers who are finding their way into the field and seek a brief account of the relevant datasets and the recent neural models and their performances.

While significant progress has been made on the task of Legal Judgment Prediction (LJP) in recent years, the incorrect predictions made by SOTA LJP models can be attributed in part to their failure to (1) locate the key event information that determines the judgment, and (2) exploit the cross-task consistency constraints that exist among the subtasks of LJP.To address these weaknesses, we propose EPM, an Event-based Prediction Model with constraints, which surpasses existing SOTA models in performance on a standard LJP dataset.

Automatic legal judgment prediction (LJP) has recently received increasing attention in the natural language processing community in part because of its practical values as well as the associated research challenges. We present an overview of the major milestones made in LJP research covering multiple jurisdictions and multiple languages, and conclude with promising future research directions.

Legal judgment prediction, which aims at predicting judgment results such as penalty, charges, and statutes for cases, has attracted much attention recently. In this article, we focus on building a recommender system to predict the associated statutes for a case given the facts of the case as input. For this purpose, we propose a two-step neural network-based machine learning framework to assist judges as well as ordinary people to reduce their effort in finding applicable statutes. The proposed model takes advantage of recurrent neural networks with a max-pooling layer to obtain contextual representations of documents, i.e., the facts associated with the cases. Moreover, an attention mechanism is used to automatically focus on the important words contributing to the prediction of statutes. In addition, we apply an encoder--decoder ranking approach to extract correlations between statutes to achieve more accurate recommendation results. We evaluate our model on a real-world dataset. Experimental results show that, compared with existing baseline methods, our method can predict statutes that are more likely to appear in real judgments.

The task of statute prediction focuses on determining applicable statutes for legal cases with the inputs of fact descriptions, which is crucial for both legal experts and ordinary people without professional knowledge. Existing works just consider the correspondence from facts to individual statutes and ignore the correlations between statutes. Moreover, charges of cases have associations with statutes. To address these issues, we formulate statute prediction task as a sequence generation problem and propose a novel joint generative model to mine correlations between statutes. By integrating statute prediction task and charge prediction task, we also make model learn associations between statutes and charges. Experiments show our model outperforms several baselines significantly and correlative statutes are predicted accurately.

In the case of a traditional court judge, the facts are based on the law as the cornerstone, the fact that can be proved by the legal evidences. As we all know, assisting judges to manage evidence chain information can significantly improve the efficiency and quality of judges. Therefore, based on this idea, this paper will introduce the design and implementation of Visual Modeling Tool for evidence chain. The tool can help the judge to build various types of evidence chain, and can help to improve the work efficiency of judges. This visual modeling tool is divided into two main forms of visualization, includes the Graphical Mode and Table Mode. It means the same data with different display forms. So that the judge can deal with a large number of complex and varied evidence of chain information quickly and easily. Also, the efficiency of the judge to handle the case can be significantly improved.

This paper presents a method of the association statistics between the cause of action and the statute. According to the close relationship between the cause of action and the statute in the written judgment, this paper puts forward the statistical analysis of the cause of action and the statute. The method mainly includes the pretreatment of semi-structured written judgments, reading information of the cause of action and the statute from structured documents, standardizing statutes, depositing in the database, generating EXCEL form of the association statistics from the cause of action to the statue and generating TXT form of the association statistics from the statue to the cause of action. In the process of reasoning and assessment, we can achieve the prediction of statutes and narrow the size of the cause of action.

With the continuous advancement of the informatization of the Chinese People's Court, the court's view on the extraction and application of information has not only been on the structured data, but also for the semi-structured and unstructured data. In the process of in-depth study of the judgment document, many cases require the collection of the document result as an important data dimension, and the key is that the statute is the core of the whole result, so the integrity and correctness of the statute obtained has played a key role for the process of the judgment document processing. However, in the process of writing a specific judgment document, the same statute has different string forms due to the diversity of writing, which leads directly to the error data source. Comparing the editing distance between the strings can judge the similarity of them to a certain extent. Therefore, an automatic method based on the editing distance algorithm is devised, which constructs the disparity model between different statutes strings, to obtain the standardized writing of the same type data. Using this method can remove the non-standard writing of statutes, and ultimately access to the standard statutes collection. This method has a higher efficiency than the method of enumerating all the writing circumstances, which needs the manual participation, additional data storage and update.

The traditional approach to measure text similarity is based on the TF-IDF algorithm to get the document vector, and then use the cosine similarity algorithm to calculate the text similarity. However, this method of statistical way ignores the potential semantics of the articles or words. By some means, this method only aims at the word itself. But with the Latent Semantic Analysis, the semantic space is added on the basis of calculate TF-IDF. Each word and document can have a position in semantic space by Singular Value Decomposition. That allows the semantic analysis, document clustering, and the relationship between semantic class and document class can be finished at the same time. Here, we summarize the text similarity measures, and gradually extend to the Latent Semantic Analysis. The experiment shows that the statutes predicted by LSA are more accurate than that only by TF-IDF.