Search Results for author: Shashank Sonkar

Found 12 papers, 4 papers with code

Pedagogical Alignment of Large Language Models

1 code implementation7 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.

Code Soliloquies for Accurate Calculations in Large Language Models

1 code implementation21 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.

Language Modelling Large Language Model +1

Deduction under Perturbed Evidence: Probing Student Simulation Capabilities of Large Language Models

no code implementations23 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).

StrategyQA valid

Investigating the Role of Feed-Forward Networks in Transformers Using Parallel Attention and Feed-Forward Net Design

no code implementations22 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.

CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles

1 code implementation22 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).

Chatbot Decision Making

A Visual Tour Of Current Challenges In Multimodal Language Models

no code implementations22 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.

Text-to-Image Generation Visual Grounding

Embedding models through the lens of Stable Coloring

no code implementations29 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.

Denoising

NePTuNe: Neural Powered Tucker Network for Knowledge Graph Completion

1 code implementation15 Apr 2021 Shashank Sonkar, Arzoo Katiyar, Richard G. Baraniuk

Knowledge graphs link entities through relations to provide a structured representation of real world facts.

Link Prediction

Attention Word Embedding

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.

Sentence Word Similarity

qDKT: Question-centric Deep Knowledge Tracing

no code implementations25 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.

Knowledge Tracing Language Modelling

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