Graph rationalization methods aim to improve the explainability of Graph Neural Networks by identifying critical subgraphs (rationales) for task prediction. Motivated by increasing concerns over data privacy, federated graph rationalization has recently gained traction as a novel research area. However, in federated settings, data heterogeneity across clients exacerbates shortcut learning, where models rely on spurious and client-specific features rather than invariant causal rationales. Existing solutions, such as environment-aware data augmentation, suffer from low-quality environment representations. To address this, we propose DiffGR, a Diff erence-based sample selection strategy for federated Graph Rationalization. DiffGR selects samples where local and global models exhibit the highest prediction discrepancies, as these likely reflect strong shortcut reliance, enabling more accurate environment representations. Additionally, we introduce a mutual information (MI) inspired environment-conditioned data augmentation method that minimizes MI between environments and predictions while maximizing MI between rationales and predictions. Experiments on real-world and synthetic datasets demonstrate the effectiveness of DiffGR in improving rationale quality and model robustness in federated settings. Code is available at https://github.com/yuelinan/Codes-of-DiffGR.

Graph In-Context Learning, with the ability to adapt pre-trained graph models to novel and diverse downstream graphs without updating any parameters, has gained much attention in the community. The key to graph in-context learning is to perform downstream graphs conditioned on chosen prompt examples. Existing methods randomly select subgraphs or edges as prompts, leading to noisy graph prompts and inferior model performance. Additionally, due to the gap between pre-training and testing graphs, when the number of classes in the testing graphs is much greater than that in the training, the in-context learning ability will also significantly deteriorate. To tackle the aforementioned challenges, we develop a multi-stage adaptive prompt optimization method GraphPrompter, which optimizes the entire process of generating, selecting, and using graph prompts for better in-context learning capabilities. Firstly, Prompt Generator introduces a reconstruction layer to highlight the most informative edges and reduce irrelevant noise for graph prompt construction. Furthermore, in the selection stage, Prompt Selector employs the k-nearest neighbors algorithm and pre-trained selection layers to dynamically choose appropriate sam-ples and minimize the influence of irrelevant prompts. Finally, we leverage a Prompt Augmenter with a cache replacement strategy to enhance the generalization capability of the pre-trained model on new datasets. Extensive experiments show that GraphPrompter effectively enhances the in-context learning ability of graph models. On average across all the settings, our approach surpasses the state-of-the-art baselines by over 8 %. Our code is released at https://ithub.com/karin0018/GraphPrompter.

Programming Knowledge Tracking (PKT) aims to dynamically diagnose learners' mastery levels of programming knowledge based on their coding activities, facilitating more effective and personalized programming education. However, current PKT studies primarily focus on the implicit relationship between code content and knowledge assessment, often overlooking two types of noise signals in long-term programming activities: unwanted signals from unrelated submissions and weak signals from minor modifications. This practical challenge significantly limits model performance and application. To address this issue, we propose Coda, a Code graph-based tuning adaptor designed to enhance existing PKT models by identifying and mitigating the impact of noise. Specifically, Coda first transforms the loose code sequences submitted by each learner into a compact code graph. By leveraging this code graph, unwanted signals can be identified from a semantic similarity perspective. We then apply a cluster-aware GCN to the code graph, which improves the discrimination of weak signals and enables their clustering for identification. Finally, a lightweight yet effective adaptor is incorporated into the PKT task through optimization with two noise feature-based constraints and a navigational regularization term, to correct knowledge states affected by noise. It is worth mentioning that the Coda framework is model-agnostic and can be adapted to most existing PKT solutions. Extensive experimental results on four real-world datasets demonstrate that Coda effectively performs the PKT task in the presence of noisy programming records, outperforming typical baselines.

In digital education, Cognitive Diagnosis (CD) is essential for modeling learners' cognitive states, such as problem-solving ability and knowledge proficiency, by analyzing their response data, like answer correctness. However, traditional CD methods struggle with effectiveness and efficiency. They fail to capture the diversity and uncertainty of learners' cognitive states. Additionally, response prediction can be time-consuming. To address these issues, we propose BoxCD, a contrastive probabilistic box embedding model for cognitive diagnosis. BoxCD utilizes high-dimensional axis-aligned hyper-rectangles (boxes) to represent learners and exercises, with the volume of intersecting boxes used to predict learners' responses. This approach effectively captures semantic diversity and uncertainty while enhancing diagnostic effectiveness. To stabilize box embeddings, we integrate contrastive learning objectives with response prediction goals, optimizing the distance between positive and negative samples of learner and exercise boxes to improve uniformity. Additionally, we develop a rank-based response prediction method that leverages the geometric properties of box embeddings to assess learners' response correctness efficiently. Comprehensive experiments on two real-world datasets demonstrate that BoxCD outperforms traditional CD models in effectiveness and efficiency. This showcases its potential to enhance personalized learning in digital education platforms.

The success of Graph Neural Networks (GNNs) in graph classification has heightened interest in explainable GNNs, particularly through graph rationalization. This method aims to enhance GNNs explainability by identifying subgraph structures (i.e., rationales) that support model predictions. However, existing methods often rely on centralized datasets, posing challenges in scenarios where data privacy is crucial, such as in molecular property prediction. Federated Learning (FL) offers a solution by enabling collaborative model training without sharing raw data. In this context, Federated Graph Rationalization emerges as a promising research direction. However, in each client, the rationalization methods often rely on client-specific shortcuts to compose rationales and make task predictions. Data heterogeneity, characterized by non-IID data across clients, exacerbates this problem, leading to poor prediction performance. To address these challenges, we propose the Environment-aware Data Augmentation (EaDA) method for Federated Graph Rationalization. EaDA comprises two main components: the Environment-aware Rationale Extraction (ERE) module and the Local-Global Alignment (LGA) module. The ERE module employs prototype learning to infer and share abstract environment information across clients, which are then aggregated to form a global environment. This information is used to generate counterfactual samples for local clients, enhancing the robustness of task predictions. The LGA module uses contrastive learning methods to align local and global rationale representations, mitigating performance degradation due to data heterogeneity. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of our approaches. Code is available at https://github.com/yuelinan/Codes-of-EaDA.

Learner modeling is a crucial task in online learning environments, where Cognitive Diagnosis Models (CDMs) are employed to assess learners' knowledge mastery levels based on recorded response logs. However, the prevalence of noise in recorded response data poses significant challenges, including various behaviors such as guess and slip, casual answers, and system-induced errors. The existence of noise degrades the accuracy of diagnosis results and learner performance predictions. In this work, we propose a general framework, Adaptive Response Denoising (AdaRD), designed to salvage CDMs from the influence of noisy learner-exercise responses. AdaRD extends existing CDMs, incorporating primary training for denoised CDMs and auxiliary training for additional denoising support. The primary training employs binary Generalized Cross Entropy (GCE) loss to slow down the large update of learner knowledge states caused by noisy responses. Simultaneously, we utilize the variance of diagnosed knowledge mastery levels between primary and auxiliary diagnosis modules as a criterion to downweight high-variance responses that are likely to be noisy. In this manner, the proposed framework can prune noisy response learning during training, thereby enhancing the accuracy and robustness of CDMs. Extensive experiments on both real-world and synthetic datasets validate AdaRD's effectiveness in mitigating the impact of noisy learner-exercise responses.

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.

Deep Neural Networks (DNNs), despite their notable progress across information retrieval tasks, encounter the issues of shortcut learning and struggle with poor generalization due to their reliance on spurious correlations between features and labels. Current research mainly mitigates shortcut learning behavior using augmentation and distillation techniques, but these methods could be laborious and introduce unwarranted biases. To tackle these, in this paper, we propose COMI, a novel method to COrrect and MItigate shortcut learning behavior. Inspired by the ways students solve shortcuts in educational scenarios, we aim to reduce model's reliance on shortcuts and enhance its ability to extract underlying information integrated with standard Empirical Risk Minimization (ERM). Specifically, we first design Correct Habit (CoHa) strategy to retrieve the top m challenging samples for priority training, which encourages model to rely less on shortcuts in the early training. Then, to extract more meaningful underlying information, the information derived from ERM is separated into task-relevant and task-irrelevant information, the former serves as the primary basis for model predictions, while the latter is considered non-essential. However, within task-relevant information, certain potential shortcuts contribute to overconfident predictions. To mitigate this, we design Deep Mitigation (DeMi) network with shortcut margin loss to adaptively control the feature weights of shortcuts and eliminate their influence. Besides, to counteract unknown shortcut tokens issue in NLP, we adopt locally interpretable module-LIME to help recognize shortcut tokens. Finally, extensive experiments conducted on NLP and CV tasks demonstrate the effectiveness of COMI, which can perform well on both IID and OOD samples.

Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data environment, yet accessing the environment information is complex. Besides, another promising approach involves rationalization, extracting invariant rationales for predictions. However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of theclassification and therationalization module. Specifically, we first assume that multiple environments are available in theclassification module. Then, we introduce diverse training distributions using an environment-conditional generative network, enabling robust graph representations. Meanwhile, therationalization module employs a separator to identify relevant rationale subgraphs while the remaining non-rationale subgraphs are de-correlated with labels. Next, we align graph representations from theclassification module with rationale subgraph representations using the knowledge distillation methods, enhancing the learning signal for rationales. Finally, we infer multiple environments by gathering non-rationale representations and incorporate them into theclassification module for cooperative learning. Extensive experimental results on both benchmarks and synthetic datasets demonstrate the effectiveness of C2R. Code is available at https://github.com/yuelinan/Codes-of-C2R.

Massive legal documents have promoted the application of legal intelligence. Among them, Legal Judgment Prediction (LJP) has emerged as a critical task, garnering significant attention. LJP aims to predict judgment results for multiple subtasks, including charges, law articles, and terms of penalty. Existing studies primarily focus on utilizing the entire factual description to produce judgment results, overlooking the practical judicial scenario where judges consider various crime circumstances to decide verdicts and sentencing. To this end, in this paper, we propose a circumstance-aware LJP framework (i.e., NeurJudge) by exploring the circumstances of crime. Specifically, NeurJudge first separates the factual description into different circumstances with the predicted results of intermediate subtasks and then employs them to yield results of other subtasks. Besides, as confusing verdicts may degrade the performance of LJP, we further develop a variant of NeurJudge (NeurJudge+) that incorporates the semantics of labels (charges and law articles) into facts to yield more expressive and distinguishable fact representations. Finally, to provide explanations for LJP, we extend NeurJudge to an explainable LJP framework E-NeurJudge with a cooperative teacher-student system. The teacher system is NeurJudge which exploits legal particularities well but lacks explanation capability. The student system is a rationalization method that provides explainability but fails to utilize legal particularities. To combine the advantages of the above methods, we use a transferring function to transfer legal particularities from the teacher to the student, making a trade-off between yielding LJP results and rendering them explainable. Extensive experimental results on real-world datasets validate the effectiveness of our proposed frameworks.

Cognitive diagnosis (CD) aims to reveal the proficiency of students on specific knowledge concepts and traits of test exercises (e.g., difficulty). It plays a critical role in intelligent education systems by supporting personalized learning guidance. However, recent developments in CD mostly concentrate on improving the accuracy of diagnostic results and often overlook the important and practical task: domain-level zero-shot cognitive diagnosis (DZCD). The primary challenge of DZCD is the deficiency of student behavior data in the target domain due to the absence of student-exercise interactions or unavailability of exercising records for training purposes. To tackle the cold-start issue, we propose a two-stage solution named TechCD (Transferable knowledgE Concept grapH embedding framework for Cognitive Diagnosis). The fundamental notion involves utilizing a pedagogical knowledge concept graph (KCG) as a mediator to connect disparate domains, allowing the transmission of student cognitive signals from established domains to the zero-shot cold-start domain. Specifically, a naive yet effective graph convolutional network (GCN) with the bottom-layer discarding operation is initially employed over the KCG to learn transferable student cognitive states and domain-specific exercise traits. Moreover, we give three implementations of the general TechCD framework following the typical cognitive diagnosis solutions. Finally, extensive experiments on real-world datasets not only prove that Tech can effectively perform zero-shot diagnosis, but also give some popular applications such as exercise recommendation.

Civil Case Judgment Prediction (CCJP) is a fundamental task in the legal intelligence of the civil law system, which aims to automatically predict the judgment results on each plea of the plaintiff. Existing studies mainly focus on making judgment predictions only on a certain civil cause (e.g., the divorce dispute) by utilizing the fact descriptions and pleas of the plaintiff, which still suffer from the various causes and complicated legal essential elements in the real court. Thus, in this paper, we formalize CCJP as a multi-task learning problem and propose a CCJP method centering on the trial mode of essential elements, CPEE, which explores the practical judicial process and analyzes comprehensive legal essential elements to make judgment predictions. Specifically, we first construct three tasks (i.e., the predictions on the civil causes, law articles, and the final judgment on each plea) necessary for CCJP, that follow the judgment process and exploit the results of intermediate subtasks to make judgment predictions. Then we design a logic-enhanced network to predict the results of three tasks and conduct a comprehensive study of civil cases. Finally, owing to the interlinked and dependent relationships among each task, we adopt the cause prediction result to help predict law articles and incorporate them into final judgment prediction through a gate mechanism. Furthermore, since the existing dataset fails to provide sufficient case information, we construct a real-world CCJP dataset that contains various causes and comprehensive legal elements. Extensive experimental results on the dataset validate the effectiveness of our method.

Assessing the proficiency of trial lawyers in different legal fields is of significant importance since a qualified lawyer or lawyer team can strive for his clients' best rights while ensuring the fairness of litigations. However, proficiency assessment for lawyers is very challenging due to many technical and domain challenges, such as the lack of unified evaluation standards, and the complex interactions between lawyers and cases in real legal systems. To this end, we propose a novel proficiency assessment network for trial lawyers (LawyerPAN) to quantify lawyer proficiency through online litigation records. Specifically, we first leverage the theories in psychological measurement for mapping the proficiency of lawyers in each field into a unified real number space. Meanwhile, the characteristics of cases (i.e., case difficulty and discrimination) are well modeled to ensure fairness when assessing lawyers in different cases and fields. Then, we model the interactions between lawyers and cases from two perspectives: the anticipatory perspective aims to measure the personal proficiency of anticipated strategy, and the adversarial perspective seeks to depict the gap of lawyers' proficiency between both sides (i.e., plaintiffs and defendants). Finally, we conduct extensive experiments on real-world data, and the results show the effectiveness and interpretability of our approaches on assessing the proficiency of trial lawyers.

Legal Judgment Prediction is a fundamental task in legal intelligence of the civil law system, which aims to automatically predict the judgment results of multiple subtasks, such as charge, law article, and term of penalty prediction. Existing studies mainly focus on the impact of the entire fact description on all subtasks. They ignore the practical judicial scenario, where judges adopt circumstances of crime (i.e., various parts of the fact) to decide judgment results. To this end, in this paper, we propose a circumstance-aware legal judgment prediction framework (i.e., NeurJudge) by exploring circumstances of crime. Specifically, NeurJudge utilizes the results of intermediate subtasks to separate the fact description into different circumstances and exploits them to make the predictions of other subtasks. In addition, considering the popularity of confusing verdicts (i.e., charges and law articles), we further extend NeurJudge to a more comprehensive framework which is denoted by NeurJudge+. Particularly, NeurJudge+ utilizes a label embedding method to incorporate the semantics of labels (i.e., charges and law articles) into facts to generate more expressive fact representations for confusing verdicts problems. Extensive experimental results on two real-world datasets clearly validate the effectiveness of our proposed frameworks.

Criminal Court View Generation is an essential task in legal intelligence, which aims to automatically generate sentences interpreting judgment results. The court view could be seen as the summary of crime circumstances in a case, including ADjudging Circumstance (ADC) and SEntencing Circumstance (SEC). However, different circumstances vary widely, and adopting them to generate court views directly may limit the generation performance. Therefore, it is necessary to identify the ADC and SEC related sentences in case facts and enhance them into the court view generation, respectively. To this end, in this paper, we propose a novel Circumstances enhanced Criminal Court View Generation (C3VG) method, consisting of the extraction and generation stage. Specifically, in the extraction stage, we design a Circumstances Selector to select ADC and SEC related sentences. After that, we apply them to two generators to generate the circumstances enhanced court views, respectively. After merging the two types of court views, we could obtain the final court views. We evaluate C3VG by conducting extensive experiments on a real-world dataset and experimental results clearly validate the effectiveness of our proposed model.