Harnessing Large Language Models for Legal Tasks: Opportunities and Challenges
Large language models are transforming the legal domain by enabling novel applications in legal text processing, case retrieval and analysis, education, and legal practice, while also posing challenges related to biases, hallucination, and alignment with fundamental legal values.
Common Law Court Judgment Summarization Dataset Creation and Evaluation
Creating a dataset for multi-jurisdictional common law court judgment summarization and evaluating the performance of various models.
SaulLM-7B: A Pioneering Large Language Model for the Legal Domain
SaulLM-7B is a groundbreaking large language model specifically designed for legal text comprehension and generation, offering state-of-the-art proficiency in processing legal documents.
SimuCourt: Building Judicial Decision-Making Agents with Real-world Judgement Documents
SimuCourt introduces a benchmark for evaluating judicial decision-making agents and proposes a multi-agent framework, AgentsCourt, to simulate court processes.
Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizers
AI-generated summaries improve public understanding and accessibility of court opinions.
A Path Towards Legal Autonomy: Interoperable Legal Information Processing
Achieving legal autonomy for AI agents through interoperable and explainable methods using large language models, expert systems, and Bayesian networks.
DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment
Introducing DELTA, a discriminative model for legal case retrieval, focusing on key facts for improved relevance determination.
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval
Using large language models for automated relevance judgments in legal case retrieval.
Exploring GPTs in Cross-Lingual Legal QA Systems
GPTs' performance in cross-lingual legal QA scenarios.
Enhancing Legal Document Retrieval: Multi-Phase Approach with Large Language Models
Maximizing retrieval accuracy through multi-phase approach with large language models.
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