no code implementations • 23 Apr 2024 • Shashank Sonkar, Naiming Liu, Richard G. Baraniuk
This paper presents a novel exploration into the regressive side effects of training Large Language Models (LLMs) to mimic student misconceptions for personalized education.
1 code implementation • 22 Apr 2024 • Shashank Sonkar, Kangqi Ni, Lesa Tran Lu, Kristi Kincaid, John S. Hutchinson, Richard G. Baraniuk
With this work, we offer a fresh perspective on grading long, fact-based answers and introduce a new dataset to stimulate further research in this important area.
no code implementations • 22 Apr 2024 • Shashank Sonkar, Naiming Liu, Debshila B. Mallick, Richard G. Baraniuk
We subsequently train language models to identify entailment, contradiction, and neutrality from student response, akin to NLI, and with the added dimension of identifying omissions from gold answers.
1 code implementation • 7 Feb 2024 • Shashank Sonkar, Kangqi Ni, Sapana Chaudhary, Richard G. Baraniuk
Building on this perspective, we propose a novel approach for constructing a reward dataset specifically designed for the pedagogical alignment of LLMs.
no code implementations • 3 Oct 2023 • Naiming Liu, Shashank Sonkar, Zichao Wang, Simon Woodhead, Richard G. Baraniuk
We propose novel evaluations for mathematical reasoning capabilities of Large Language Models (LLMs) based on mathematical misconceptions.
1 code implementation • 21 Sep 2023 • Shashank Sonkar, MyCo Le, Xinghe Chen, Naiming Liu, Debshila Basu Mallick, Richard G. Baraniuk
Our approach notably enhances the quality of synthetic conversation datasets, especially for subjects that are calculation-intensive.
no code implementations • 23 May 2023 • 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).
no code implementations • 22 May 2023 • 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 comparing it to their Series Attention and Feed-Forward Net Design (SAF) counterparts.
1 code implementation • 22 May 2023 • Shashank Sonkar, Naiming Liu, Debshila Basu Mallick, Richard G. Baraniuk
We present a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) for building advanced Intelligent Tutoring Systems (ITS) powered by high-performance Large Language Models (LLMs).
no code implementations • 19 Dec 2022 • Shashank Sonkar, Zichao Wang, Richard G. Baraniuk
MANER re-purposes the <mask> token for NER prediction.
no code implementations • 22 Oct 2022 • Shashank Sonkar, Naiming Liu, Richard G. Baraniuk
Transformer models trained on massive text corpora have become the de facto models for a wide range of natural language processing tasks.
no code implementations • 29 Sep 2021 • Aditya Desai, Shashank Sonkar, Anshumali Shrivastava, Richard Baraniuk
Grounded on this framework, we show that many algorithms ranging across different domains are, in fact, searching for continuous stable coloring solutions of an underlying graph corresponding to the domain.
1 code implementation • 15 Apr 2021 • Shashank Sonkar, Arzoo Katiyar, Richard G. Baraniuk
Knowledge graphs link entities through relations to provide a structured representation of real world facts.
Ranked #11 on Link Prediction on FB15k-237
no code implementations • COLING 2020 • Shashank Sonkar, Andrew E. Waters, Richard G. Baraniuk
Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models.
no code implementations • 25 May 2020 • Shashank Sonkar, Andrew E. Waters, Andrew S. Lan, Phillip J. Grimaldi, Richard G. Baraniuk
Knowledge tracing (KT) models, e. g., the deep knowledge tracing (DKT) model, track an individual learner's acquisition of skills over time by examining the learner's performance on questions related to those skills.