activity
20212024
most citedUnlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts

44 citations · 141 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CL202413 cited

Understanding the Role of Temperature in Diverse Question Generation by GPT-4

Arav Agarwal, Karthik Mittal, Aidan Doyle +5

We conduct a preliminary study of the effect of GPT's temperature parameter on the diversity of GPT4-generated questions. We find that using higher temperature values leads to sign…

cs.CY20231 cited

From GPT-3 to GPT-4: On the Evolving Efficacy of LLMs to Answer Multiple-choice Questions for Programming Classes in Higher Education

Jaromir Savelka, Arav Agarwal, Christopher Bogart +1

We explore the evolving efficacy of three generative pre-trained transformer (GPT) models in generating answers for multiple-choice questions (MCQ) from introductory and intermedia…

cs.CL20232 cited

From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems

Samyar Janatian, Hannes Westermann, Jinzhe Tan +2

Encoding legislative text in a formal representation is an important prerequisite to different tasks in the field of AI & Law. For example, rule-based expert systems focused on leg…

cs.CY20234 cited

Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models

Jaromir Savelka, Paul Denny, Mark Liffiton +1

The accurate classification of student help requests with respect to the type of help being sought can enable the tailoring of effective responses. Automatically classifying such r…

cs.AI20235 cited

Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies

Jakub Drápal, Hannes Westermann, Jaromir Savelka

Thematic analysis and other variants of inductive coding are widely used qualitative analytic methods within empirical legal studies (ELS). We propose a novel framework facilitatin…

cs.CY20239 cited

CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes

Mark Liffiton, Brad Sheese, Jaromir Savelka +1

Computing educators face significant challenges in providing timely support to students, especially in large class settings. Large language models (LLMs) have emerged recently and…