6 citations · 20 across the 29 of their papers we have counts for
6 papers · 2 filters
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education
Naiming Liu, Shashank Sonkar, Myco Le +1
This paper introduces MalAlgoQA, a novel dataset designed to evaluate the counterfactual reasoning capabilities of Large Language Models (LLMs) through a pedagogical approach. The…
Many-Shot Regurgitation (MSR) Prompting
Shashank Sonkar, Richard G. Baraniuk
We introduce Many-Shot Regurgitation (MSR) prompting, a new black-box membership inference attack framework for examining verbatim content reproduction in large language models (LL…
Automated Long Answer Grading with RiceChem Dataset
Shashank Sonkar, Kangqi Ni, Lesa Tran Lu +3
We introduce a new area of study in the field of educational Natural Language Processing: Automated Long Answer Grading (ALAG). Distinguishing itself from Automated Short Answer Gr…
Marking: Visual Grading with Highlighting Errors and Annotating Missing Bits
Shashank Sonkar, Naiming Liu, Debshila B. Mallick +1
In this paper, we introduce "Marking", a novel grading task that enhances automated grading systems by performing an in-depth analysis of student responses and providing students w…
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning
Shashank Sonkar, Naiming Liu, Richard G. Baraniuk
The pursuit of personalized education has led to the integration of Large Language Models (LLMs) in developing intelligent tutoring systems. To better understand and adapt to indiv…
Pedagogical Alignment of Large Language Models
Shashank Sonkar, Kangqi Ni, Sapana Chaudhary +1
Large Language Models (LLMs), when used in educational settings without pedagogical fine-tuning, often provide immediate answers rather than guiding students through the problem-so…