1.3k citations · 4.9k across the 150 of their papers we have counts for
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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…
MultiQG-TI: Towards Question Generation from Multi-modal Sources
Zichao Wang, Richard Baraniuk
We study the new problem of automatic question generation (QG) from multi-modal sources containing images and texts, significantly expanding the scope of most of the existing work…
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…
MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages
Shashank Sonkar, Zichao Wang, Richard G. Baraniuk
This paper investigates the problem of Named Entity Recognition (NER) for extreme low-resource languages with only a few hundred tagged data samples. NER is a fundamental task in N…