1.3k citations · 4.9k across the 150 of their papers we have counts for
162 papers
A Primal-Dual Framework for Transformers and Neural Networks
Tan M. Nguyen, Tam Nguyen, Nhat Ho +3
Self-attention is key to the remarkable success of transformers in sequence modeling tasks including many applications in natural language processing and computer vision. Like neur…
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…
Self-Consuming Generative Models Go MAD
Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi +5
Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this…
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…