4 citations · 13 across the 32 of their papers we have counts for
7 papers · 1 filter
Synthetic Context Generation for Question Generation
Naiming Liu, Zichao Wang, Richard Baraniuk
Despite rapid advancements in large language models (LLMs), QG remains a challenging problem due to its complicated process, open-ended nature, and the diverse settings in which qu…
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…
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals
Tam Nguyen, Tan M. Nguyen, Richard G. Baraniuk
Transformers have achieved remarkable success in a wide range of natural language processing and computer vision applications. However, the representation capacity of a deep transf…
Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions
Naiming Liu, Shashank Sonkar, Zichao Wang +2
We propose novel evaluations for mathematical reasoning capabilities of Large Language Models (LLMs) based on mathematical misconceptions. Our primary approach is to simulate LLMs…