Search Results for author:
Found 9 papers, 2 papers with code
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).
Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models
Legal texts routinely use concepts that are difficult to understand.
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.
