6 citations · 20 across the 29 of their papers we have counts for
5 papers · 1 filter
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…
Deduction under Perturbed Evidence: Probing Student Simulation Capabilities of Large Language Models
Shashank Sonkar, Richard G. Baraniuk
We explore whether Large Language Models (LLMs) are capable of logical reasoning with distorted facts, which we call Deduction under Perturbed Evidence (DUPE). DUPE presents a uniq…
CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles
Shashank Sonkar, Naiming Liu, Debshila Basu Mallick +1
We present a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) for building advanced Intelligent Tutoring Systems (ITS) powered by hig…