565 citations · 594 across the 6 of their papers we have counts for
6 papers
Can LLMs Identify Gaps and Misconceptions in Students' Code Explanations?
Priti Oli, Rabin Banjade, Andrew M. Olney +1
This paper investigates various approaches using Large Language Models (LLMs) to identify gaps and misconceptions in students' self-explanations of specific instructional material,…
Explaining Code Examples in Introductory Programming Courses: LLM vs Humans
Arun-Balajiee Lekshmi-Narayanan, Priti Oli, Jeevan Chapagain +4
Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approach…
Automated Assessment of Students' Code Comprehension using LLMs
Priti Oli, Rabin Banjade, Jeevan Chapagain +1
Assessing student's answers and in particular natural language answers is a crucial challenge in the field of education. Advances in machine learning, including transformer-based m…
The Behavior of Large Language Models When Prompted to Generate Code Explanations
Priti Oli, Rabin Banjade, Jeevan Chapagain +1
This paper systematically investigates the generation of code explanations by Large Language Models (LLMs) for code examples commonly encountered in introductory programming course…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…