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Found 13 papers, 2 papers with code
A Path Towards Legal Autonomy: An interoperable and explainable approach to extracting, transforming, loading and computing legal information using large language models, expert systems and Bayesian networks
Legal autonomy - the lawful activity of artificial intelligence agents - can be achieved in one of two ways.
From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems
We use LLMs to create pathways from legislation, according to the JusticeBot methodology for legal decision support systems, evaluate the pathways and compare them to manually created pathways.
Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies
We propose a novel framework facilitating effective collaboration of a legal expert with a large language model (LLM) for generating initial codes (phase 2 of thematic analysis), searching for themes (phase 3), and classifying the data in terms of the themes (to kick-start phase 4).
LLMediator: GPT-4 Assisted Online Dispute Resolution
In this article, we introduce LLMediator, an experimental platform designed to enhance online dispute resolution (ODR) by utilizing capabilities of state-of-the-art large language models (LLMs) such as GPT-4.
JusticeBot: A Methodology for Building Augmented Intelligence Tools for Laypeople to Increase Access to Justice
We also present an interface to build tools using this methodology and a case study of the first deployed JusticeBot version, focused on landlord-tenant disputes, which has been used by thousands of individuals.
Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?
We observed that, with a relatively minor decrease in performance, GPT-4 can perform batch predictions leading to significant cost reductions.
Explaining Legal Concepts with Augmented Large Language Models (GPT-4)
We compare the performance of a baseline setup, where GPT-4 is directly asked to explain a legal term, to an augmented approach, where a legal information retrieval module is used to provide relevant context to the model, in the form of sentences from case law.
Toward an Intelligent Tutoring System for Argument Mining in Legal Texts
We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user.
Data-Centric Machine Learning in the Legal Domain
The results also indicate that enhancements to a data set could be considered, alongside the advancement of the ML models, as an additional path for increasing classification performance on various tasks in AI & Law.
Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents
We use this observation in allowing annotators to quickly view and annotate sentences that are semantically similar to a given sentence, across an entire corpus of documents.
Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains
In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, legal systems (common and civil law), languages, and domains (i. e. contexts).
Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data
We analyze the ability of pre-trained language models to transfer knowledge among datasets annotated with different type systems and to generalize beyond the domain and dataset they were trained on.
Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain
In this paper, we present a method of building strong, explainable classifiers in the form of Boolean search rules.
