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Found 29 papers, 1 papers with code
Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models
Large language models with billions of parameters, such as GPT-3. 5, GPT-4, and LLaMA, are increasingly prevalent.
GPTs and Language Barrier: A Cross-Lingual Legal QA Examination
In this paper, we explore the application of Generative Pre-trained Transformers (GPTs) in cross-lingual legal Question-Answering (QA) systems using the COLIEE Task 4 dataset.
VLSP 2023 -- LTER: A Summary of the Challenge on Legal Textual Entailment Recognition
In this new era of rapid AI development, especially in language processing, the demand for AI in the legal domain is increasingly critical.
Balancing Exploration and Exploitation in LLM using Soft RLLF for Enhanced Negation Understanding
Finetuning approaches in NLP often focus on exploitation rather than exploration, which may lead to suboptimal models.
A Deep Learning-Based System for Automatic Case Summarization
This paper presents a deep learning-based system for efficient automatic case summarization.
Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applications
Language serves as a vehicle for conveying thought, enabling communication among individuals.
RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification
In this study, we present a novel and challenging multilabel Vietnamese dataset (RMDM) designed to assess the performance of large language models (LLMs), in verifying electronic information related to legal contexts, focusing on fake news as potential input for electronic evidence.
Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs
This paper presents a knowledge graph construction method for legal case documents and related laws, aiming to organize legal information efficiently and enhance various downstream tasks.
NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models
This paper describes the NOWJ1 Team's approach for the Automated Legal Question Answering Competition (ALQAC) 2023, which focuses on enhancing legal task performance by integrating classical statistical models and Pre-trained Language Models (PLMs).
Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task
The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal textual entailment.
NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment
In recent years, natural language processing has gained significant popularity in various sectors, including the legal domain.
Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichment
Question answering (QA) in law is a challenging problem because legal documents are much more complicated than normal texts in terms of terminology, structure, and temporal and logical relationships.
NOWJ at COLIEE 2023 -- Multi-Task and Ensemble Approaches in Legal Information Processing
This paper presents the NOWJ team's approach to the COLIEE 2023 Competition, which focuses on advancing legal information processing techniques and applying them to real-world legal scenarios.
LBMT team at VLSP2022-Abmusu: Hybrid method with text correlation and generative models for Vietnamese multi-document summarization
Multi-document summarization is challenging because the summaries should not only describe the most important information from all documents but also provide a coherent interpretation of the documents.
A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3
LawGPT 1. 0 is a virtual legal assistant built on the state-of-the-art language model GPT-3, fine-tuned for the legal domain.
Law to Binary Tree -- An Formal Interpretation of Legal Natural Language
First, legal reasoning can be performed on the basis of the binary tree representation of the regulations.
Attentive Deep Neural Networks for Legal Document Retrieval
The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents.
Multi-stage Information Retrieval for Vietnamese Legal Texts
This study deals with the problem of information retrieval (IR) for Vietnamese legal texts.
Toward Improving Attentive Neural Networks in Legal Text Processing
In this dissertation, we selectively present the main achievements in improving attentive neural networks in automatic legal document processing.
Transformer-based Approaches for Legal Text Processing
In this paper, we introduce our approaches using Transformer-based models for different problems of the COLIEE 2021 automatic legal text processing competition.
HYDRA -- Hyper Dependency Representation Attentions
Attention is all we need as long as we have enough data.
JNLP Team: Deep Learning Approaches for Legal Processing Tasks in COLIEE 2021
COLIEE is an annual competition in automatic computerized legal text processing.
ParaLaw Nets -- Cross-lingual Sentence-level Pretraining for Legal Text Processing
Ambiguity is a characteristic of natural language, which makes expression ideas flexible.
Sublanguage: A Serious Issue Affects Pretrained Models in Legal Domain
Legal English is a sublanguage that is important for everyone but not for everyone to understand.
SCNN: Swarm Characteristic Neural Network
Deep learning is a powerful approach with good performance on many different tasks.
Answering Legal Questions by Learning Neural Attentive Text Representation
We present a retrieval-based model for the task by learning neural attentive text representation.
JNLP Team: Deep Learning for Legal Processing in COLIEE 2020
We propose deep learning based methods for automatic systems of legal retrieval and legal question-answering in COLIEE 2020.
