6 papers
Learning in Blocks: A Multi Agent Debate Assisted Personalized Adaptive Learning Framework for Language Learning
Nicy Scaria, Silvester John Joseph Kennedy, Deepak Subramani
Most digital language learning curricula rely on discrete-item quizzes that test recall rather than applied conversational proficiency. When progression is driven by quiz performan…
Sensitivity of Small Language Models to Fine-tuning Data Contamination
Nicy Scaria, Silvester John Joseph Kennedy, Deepak Subramani
Small Language Models (SLMs) are increasingly being deployed in resource-constrained environments, yet their behavioral robustness to data contamination during instruction tuning r…
Dissecting Physics Reasoning in Small Language Models: A Multi-Dimensional Analysis from an Educational Perspective
Nicy Scaria, Silvester John Joseph Kennedy, Krishna Agarwal +2
Small Language Models (SLMs) offer privacy and efficiency for educational deployment, yet their utility depends on reliable multistep reasoning. Existing benchmarks often prioritiz…
Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective Distractors
Nicy Scaria, Silvester John Joseph Kennedy, Diksha Seth +2
Generating high-quality MCQs, especially those targeting diverse cognitive levels and incorporating common misconceptions into distractor design, is time-consuming and expertise-in…
EvalYaks: Instruction Tuning Datasets and LoRA Fine-tuned Models for Automated Scoring of CEFR B2 Speaking Assessment Transcripts
Nicy Scaria, Silvester John Joseph Kennedy, Thomas Latinovich +1
Relying on human experts to evaluate CEFR speaking assessments in an e-learning environment creates scalability challenges, as it limits how quickly and widely assessments can be c…
Can Small Language Models Learn, Unlearn, and Retain Noise Patterns?
Nicy Scaria, Silvester John Joseph Kennedy, Deepak Subramani
With the growing need for efficient language models in resource-constrained environments, Small Language Models (SLMs) have emerged as compact and practical alternatives to Large L…