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Found 22 papers, 3 papers with code
ECHR: Legal Corpus for Argument Mining
The corpus is annotated in terms of three types of clauses useful in argument mining: premise, conclusion, and non-argument parts of the text.
Understanding the Role of Temperature in Diverse Question Generation by GPT-4
We conduct a preliminary study of the effect of GPT's temperature parameter on the diversity of GPT4-generated questions.
A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education
While there is a growing body of research in computing education on utilizing large language models (LLMs) in generation and engagement with coding exercises, the use of LLMs for generating programming MCQs has not been extensively explored.
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.
Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models
This study evaluates the performance of the GPT-3. 5 and GPT-4 models for classifying help requests from students in an introductory programming class.
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).
The Robots are Here: Navigating the Generative AI Revolution in Computing Education
Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards LLMs and their use in computing education contexts.
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.
Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives
We evaluated 127 LOs that were automatically generated based on a carefully crafted prompt (detailed guidelines on high-quality LOs authoring) submitted to GPT-4 for conceptual modules and projects of an AI Practitioner course.
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.
Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses
Additionally, we analyze the assessments that were not handled well by GPT-4 to understand the current limitations of the model, as well as its capabilities to leverage feedback provided by an auto-grader.
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.
Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts
We evaluated the capability of a state-of-the-art generative pre-trained transformer (GPT) model to perform semantic annotation of short text snippets (one to few sentences) coming from legal documents of various types.
Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?
We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate Python programming courses at the postsecondary level.
Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code
While questions requiring to fill-in a blank in the code or completing a natural language statement about the snippet are handled rather successfully, MCQs that require analysis and/or reasoning about the code (e. g., what is true/false about the snippet, or what is its output) appear to be the most challenging.
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.
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.
