2 citations · 5 across the 6 of their papers we have counts for
7 papers · 1 filter
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…
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…
Code Soliloquies for Accurate Calculations in Large Language Models
Shashank Sonkar, MyCo Le, Xinghe Chen +3
High-quality conversational datasets are crucial for the successful development of Intelligent Tutoring Systems (ITS) that utilize a Large Language Model (LLM) backend. Synthetic s…
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…
Investigating the Role of Feed-Forward Networks in Transformers Using Parallel Attention and Feed-Forward Net Design
Shashank Sonkar, Richard G. Baraniuk
This paper investigates the key role of Feed-Forward Networks (FFNs) in transformer models by utilizing the Parallel Attention and Feed-Forward Net Design (PAF) architecture, and c…