Artificial intelligence has become increasingly prevalent in the legal domain. However, LegalAI systems often struggle with vague user queries that lack essential legal details, leading to suboptimal performance in practical applications. To address this challenge, we propose FactFiller, a novel approach that dynamically generates questionnaires to help users refine their input queries. Our method leverages an iterative training process that collects valuable questionnaires, eliminating the need for human annotation. Additionally, we introduce a "case-law-quiz” cascading retrieval process, ensuring that the generated questions and answer options are directly linked to specific legal provisions. Through the user study and the downstream task experiments, we demonstrate that FactFiller, while remaining easy for non-experts to understand, not only improves the completeness of queries but also ensures the performance of various domain-specific models in downstream legal tasks.

Large language models have demonstrated strong performance on general-purpose tasks but often fail to satisfy the accuracy requirements of knowledge-intensive domains such as law, medicine, and finance. Complex domain-specific generation is inherently compositional, involving multiple atomic skills such as reasoning, knowledge grounding, and numerical computation that are frequently interleaved at the token level. Existing domain adaptation methods typically train these heterogeneous skills jointly within a single objective, which makes it difficult for models to reliably coordinate multiple skills when solving complex tasks. In this work, we explicitly incorporate atomic skills into domain-specific model training and propose SplitThenMerge, a framework that decomposes domain competence into atomic skills, trains them independently, and composes them dynamically during generation. SplitThenMerge adopts a token-level sparse Mixture-of-Experts architecture to enable fine-grained skill routing and coordination while implementing each skill as a lightweight LoRA expert to achieve parameter-efficient specialization. Experimental results demonstrate that our method consistently achieves superior performance in both legal and medical domains under the same training parameter budget.

Ensuring fairness in social survey simulation is critical, as biased outputs can misrepresent underrepresented groups. This issue is growing as large language models (LLMs) are increasingly used for this task. However, standard fine-tuning based on Empirical Risk Minimization (ERM) often under-optimizes minority groups, causing substantial subgroup disparities. Distributionally robust Optimization (DRO) methods reduce worst-case errors, but their strict worst-case selection can lead to noisy and unstable optimization under demographic sparsity. These issues create intertwined challenges for fairness, convergence and stability. We propose SAFO, a dynamic utility–fairness optimization framework for LLM-based survey simulation that explicitly targets both fairness and training stability. SAFO combines (i) an Optimizer that preserves mean-loss utility, (ii) an Adversary that performs temperature-controlled, EMA-smoothed and loss-driven group reweighting, and (iii) a Nash-inspired Regulator that adaptively adjusts the utility–fairness trade-off by tracking weak-group gains and collateral utility damages. Experiments on three large-scale survey datasets from China, the U.S., and Europe show that SAFO consistently improves minority performance and social-welfare metrics. It reduces worst-group gaps by up to 12.7%, maintains overall accuracy with a mean change of less than 0.3% and lowers variance across random seeds. Our code is available at https://github.com/PiLab-ZJU/SAFO.

Despite the impressive capabilities of LLMs, they often generate content with factual inaccuracies in LegalAI, which may lead to serious legal consequences. Retrieval-Augmented Generation (RAG), a promising approach, can conveniently integrate specialized knowledge into LLMs. In practice, there are diverse legal knowledge retrieval demands (e.g. law articles and similar cases). However, existing retrieval methods are either designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. Therefore, we propose a novel **Uni**fied **L**egal **R**etriever (UniLR) capable of performing multiple legal retrieval tasks for LLMs. Specifically, we introduce attention supervision to guide the retriever in focusing on key elements during knowledge encoding. Next, we design a graph-based method to integrate meta information through a heterogeneous graph, further enriching the knowledge representation. These two components work together to enable UniLR to capture the essence of knowledge hidden beneath formats. Extensive experiments on multiple datasets of common legal tasks demonstrate that UniLR achieves the best retrieval performance and can significantly enhance the performance of LLM.

Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case. Legal judgments can involve multiple law articles and charges. Although recent methods in LJP have made notable progress, most are constrained to single-task settings (e.g., only predicting charges) or single-label settings (e.g., not accommodating cases with multiple charges), diverging from the complexities of real-world scenarios. In this paper, we address the challenge of predicting relevant law articles and charges within the framework of legal judgment prediction, treating it as a multi-task and multi-label text classification problem. We introduce a knowledge-enhanced approach, called K-LJP, that incorporates (I) ”label-level knowledge” (such as definitions and relationships among labels) to enhance the representation of case facts for each task, and (ii) ”task-level knowledge” (such as the alignment between law articles and corresponding charges) to improve task synergy. Comprehensive experiments demonstrate our method’s effectiveness in comparison to state-of-the-art (SOTA) baselines.

Legal claims refer to the plaintiff’s demands in a case and are essential to guiding judicial reasoning and case resolution. While many works have focused on improving the efficiency of legal professionals, the research on helping non-professionals (e.g., plaintiffs) remains unexplored. This paper explores the problem of legal claim generation based on the given case’s facts. First, we construct ClaimGen-CN, the first dataset for Chinese legal claim generation task, from various real-world legal disputes. Additionally, we design an evaluation metric tailored for assessing the generated claims, which encompasses two essential dimensions: factuality and clarity. Building on this, we conduct a comprehensive zero-shot evaluation of state-of-the-art general and legal-domain large language models. Our findings highlight the limitations of the current models in factual precision and expressive clarity, pointing to the need for more targeted development in this domain. To encourage further exploration of this important task, we will make the dataset publicly available.

As Large Language Models (LLMs) are widely applied in various domains, the safety of LLMs is increasingly attracting attention to avoid their powerful capabilities being misused. Existing jailbreak methods create a forced instruction-following scenario, or search adversarial prompts with prefix or suffix tokens to achieve a specific representation manually or automatically. However, they suffer from low efficiency and explicit jailbreak patterns, far from the real deployment of mass attacks to LLMs. In this paper, we point out that simply rewriting the original instruction can achieve a jailbreak, and we find that this rewriting approach is learnable and transferable. We propose the **R**ewrite to **J**ailbreak (R2J) approach, a transferable black-box jailbreak method to attack LLMs by iteratively exploring the weakness of the LLMs and automatically improving the attacking strategy. The jailbreak is more efficient and hard to identify since no additional features are introduced. Extensive experiments and analysis demonstrate the effectiveness of R2J, and we find that the jailbreak is also transferable to multiple datasets and various types of models with only a few queries. We hope our work motivates further investigation of LLM safety. The code can be found at [https://github.com/ythuang02/R2J/.](https://github.com/ythuang02/R2J/)

Information retrieval in specialized domains (e.g., legal and medical) faces challenges in aligning user queries, often expressed in colloquial language, with highly structured, terminology-rich documents. This discrepancy creates a distribution gap in the text representation. Recent methods aim to enhance queries by generating intermediary elements (e.g., keywords, pseudo-documents) before performing retrieval with large language models (LLMs). However, by treating LLMs and retrievers separately, these approaches risk producing unreliable or irrelevant intermediaries, which can significantly degrade retrieval performance. To address this issue, we propose CoEvo, an alternating optimization framework that facilitates the coevolution of LLMs and retrieval models. CoEvo operates through two key steps: L-step directs the LLM in generating intermediaries by leveraging an archive of historical examples known to enhance retrieval. R-step trains the retriever using contrastive learning on the intermediaries produced by the LLM. Finally, we evaluate and flexibly leverage content generated by the LLM to amplify the effectiveness of coevolution. Experimental results demonstrate significant improvements in retrieval performance across both legal and medical domains.

Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions. While Pretrained Language Models (PLMs) have showcased their prowess in natural language generation, their application to the complex, knowledge-intensive domain of CVG often reveals inherent limitations. In this paper, we present a novel approach, named Knowledge Injection and Guidance (KIG), designed to bolster CVG using PLMs. To efficiently incorporate domain knowledge during the training stage, we introduce a knowledge-injected prompt encoder for prompt tuning, thereby reducing computational overhead. Moreover, to further enhance the model’s ability to utilize domain knowledge, we employ a generating navigator, which dynamically guides the text generation process in the inference stage without altering the model’s architecture, making it readily transferable. Comprehensive experiments on real-world data demonstrate the effectiveness of our approach compared to several established baselines, especially in the responsivity of claims, where it outperforms the best baseline by 11.87%.

In-context learning (ICL) has emerged as a powerful tool for enhancing large language models (LLMs) in addressing downstream tasks. In this paper, we explore the vital task of example selection in ICL by mimicking the human learning process. We propose a Chain-of-Quizzes (CoQ) framework inspired by educational theories such as Bruner’s Spiral Learning and Mastery Learning theory. Specifically, our framework employs the LLMs to answer the quiz (question in the example) to sift ‘good’ examples, combines these examples iteratively with the increasing complexity, and utilizes a final exam to gauge the combined example chains. Our extensive experiments on diverse reasoning datasets show the proposed approach outperforms baseline models. These findings underscore the framework’s potential for future research.

The growing interest in leveraging large language models is driven by their exceptional imitation and reasoning capabilities. In-context learning (ICL), a streamlined method, has shown potential in boosting these models’ performance without modifying their underlying parameters, especially when supplied with suitable demonstrations. However, existing methods mainly choose demonstrations by comparing surface-level semantic similarities (e.g., based on embedding) and fall short of identifying the most fitting ones. This paper introduces the concept of a “latent learningscape”, a more nuanced representation that describes the characteristic of the demonstrations. Building on this concept, we develop a results-driven approach to characterize the latent learningscape features of demonstrations, which then inform the creation of more effective prompts. Through comprehensive testing across datasets in arithmetic, commonsense, and symbolic reasoning tasks, our approach outperforms leading models, showing an average increase in scores by 7.4 percentage points.

Court View Generation (CVG) plays a vital role in the realm of legal artificial intelligence, which aims to support judges in crafting legal judgment documents. The court view consists of three essential judgment parts: the charge-related, law article-related, and prison term-related parts, each requiring specialized legal knowledge, rendering CVG a challenging task.Although Large Language Models (LLMs) have made remarkable strides in language generation, they encounter difficulties in the knowledge-intensive legal domain.Actually, there can be two types of knowledge: internal knowledge stored within LLMs’ parameters and external knowledge sourced from legal documents outside the models.In this paper, we decompose court views into different parts, stimulate internal knowledge, and incorporate external information to unleash the power of LLMs in the CVG task.To validate our method, we conduct a series of experiment results on two real-world datasets LAIC2021 and CJO2022. The experiments demonstrate that our method is capable of generating more accurate and reliable court views.

Confusing charge prediction is a challenging task in legal AI, which involves predicting confusing charges based on fact descriptions. While existing charge prediction methods have shown impressive performance, they face significant challenges when dealing with confusing charges, such as Snatch and Robbery. In the legal domain, constituent elements play a pivotal role in distinguishing confusing charges. Constituent elements are fundamental behaviors underlying criminal punishment and have subtle distinctions among charges. In this paper, we introduce a novel From Graph to Word Bag (FWGB) approach, which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge’s reasoning process. Specifically, we first construct a legal knowledge graph containing constituent elements to help select keywords for each charge, forming a word bag. Subsequently, to guide the model’s attention towards the differentiating information for each charge within the context, we expand the attention mechanism and introduce a new loss function with attention supervision through words in the word bag. We construct the confusing charges dataset from real-world judicial documents. Experiments demonstrate the effectiveness of our method, especially in maintaining exceptional performance in imbalanced label distributions.

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.

Inquiry conversation is a common form of conversation that aims to complete the investigation (e.g., court hearing, medical consultation and police interrogation) during which a series of focus shifts occurs. While many models have been proposed to generate a smooth response to a given conversation history, neglecting the focus can limit performance in inquiry conversation where the order of the focuses plays there a key role. In this paper, we investigate the problem of response generation in inquiry conversation by taking the focus into consideration. We propose a novel Focus-aware Response Generation (FRG) method by jointly optimizing a multi-level encoder and a set of focal decoders to generate several candidate responses that correspond to different focuses. Additionally, a focus ranking module is proposed to predict the next focus and rank the candidate responses. Experiments on two orthogonal inquiry conversation datasets (judicial, medical domain) demonstrate that our method generates results significantly better in automatic metrics and human evaluation compared to the state-of-the-art approaches.

Legal judgment prediction (LJP) is a fundamental task in legal AI, which aims to assist the judge to hear the case and determine the judgment. The legal judgment usually consists of the law article, charge, and term of penalty. In the real trial scenario, the judge usually makes the decision step-by-step: first concludes the rationale according to the case’s facts and then determines the judgment. Recently, many models have been proposed and made tremendous progress in LJP, but most of them adopt an end-to-end manner that cannot be manually intervened by the judge for practical use. Moreover, existing models lack interpretability due to the neglect of rationale in the prediction process. Following the judge’s real trial logic, in this paper, we propose a novel Rationale-based Legal Judgment Prediction (RLJP) framework. In the RLJP framework, the LJP process is split into two steps. In the first phase, the model generates the rationales according to the fact description. Then it predicts the judgment based on the fact and the generated rationales. Extensive experiments on a real-world dataset show RLJP achieves the best results compared to the state-of-the-art models. Meanwhile, the proposed framework provides good interactivity and interpretability which enables practical use.

Named entity recognition (NER) is a fundamental task to recognize specific types of entities from a given sentence. Depending on how the entities appear in the sentence, it can be divided into three subtasks, namely, Flat NER, Nested NER, and Discontinuous NER. Among the existing approaches, only the generative model can be uniformly adapted to these three subtasks. However, when the generative model is applied to NER, its optimization objective is not consistent with the task, which makes the model vulnerable to the incorrect biases. In this paper, we analyze the incorrect biases in the generation process from a causality perspective and attribute them to two confounders: pre-context confounder and entity-order confounder. Furthermore, we design Intra- and Inter-entity Deconfounding Data Augmentation methods to eliminate the above confounders according to the theory of backdoor adjustment. Experiments show that our method can improve the performance of the generative NER model in various datasets.

Court’s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation. While prior text-to-text natural language generation (NLG) approaches can be used to address this problem, neglecting the confounding bias from the data generation mechanism can limit the model performance, and the bias may pollute the learning outcomes. In this paper, we propose a novel Attentional and Counterfactual based Natural Language Generation (AC-NLG) method, consisting of an attentional encoder and a pair of innovative counterfactual decoders. The attentional encoder leverages the plaintiff’s claim and fact description as input to learn a claim-aware encoder from which the claim-related information in fact description can be emphasized. The counterfactual decoders are employed to eliminate the confounding bias in data and generate judgment-discriminative court’s views (both supportive and non-supportive views) by incorporating with a synergistic judgment predictive model. Comprehensive experiments show the effectiveness of our method under both quantitative and qualitative evaluation metrics.