4 citations · 5 across the 12 of their papers we have counts for
12 papers
PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs
Yuting Huang, Yinghao Hu, Qian Xiao +7
Large language models (LLMs) have achieved remarkable success in general-domain tasks, yet their direct application to the legal domain remains challenging due to hallucinated lega…
Universal Legal Article Prediction via Tight Collaboration between Supervised Classification Model and LLM
Xiao Chi, Wenlin Zhong, Yiquan Wu +4
Legal Article Prediction (LAP) is a critical task in legal text classification, leveraging natural language processing (NLP) techniques to automatically predict relevant legal arti…
ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation
Siying Zhou, Yiquan Wu, Hui Chen +6
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 efficien…
AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios
Yuting Huang, Meitong Guo, Yiquan Wu +6
Recent advances in LegalAI have primarily focused on individual case judgment analysis, often overlooking the critical appellate process within the judicial system. Appeals serve a…
Towards Stepwise Domain Knowledge-Driven Reasoning Optimization and Reflection Improvement
Chengyuan Liu, Shihang Wang, Lizhi Qing +7
Recently, stepwise supervision on Chain of Thoughts (CoTs) presents an enhancement on the logical reasoning tasks such as coding and math, with the help of Monte Carlo Tree Search…
Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator
Chengyuan Liu, Shihang Wang, Lizhi Qing +4
Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs. But it still requires professional knowledge to facilitate the expertise for some…
Gold Panning in Vocabulary: An Adaptive Method for Vocabulary Expansion of Domain-Specific LLMs
Chengyuan Liu, Shihang Wang, Lizhi Qing +4
While Large Language Models (LLMs) demonstrate impressive generation abilities, they frequently struggle when it comes to specialized domains due to their limited domain-specific k…
RexUniNLU: Recursive Method with Explicit Schema Instructor for Universal NLU
Chengyuan Liu, Shihang Wang, Fubang Zhao +5
Information Extraction (IE) and Text Classification (CLS) serve as the fundamental pillars of NLU, with both disciplines relying on analyzing input sequences to categorize outputs…
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction
Ang Li, Qiangchao Chen, Yiquan Wu +4
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…
Enhancing Court View Generation with Knowledge Injection and Guidance
Ang Li, Yiquan Wu, Yifei Liu +3
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…
Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration
Yiquan Wu, Siying Zhou, Yifei Liu +6
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…
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction
Leilei Gan, Baokui Li, Kun Kuang +5
Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case's charge, law article and penalty term. A core problem of LJP is how to di…