5 papers
Methodologies for Improving the Quality of AI Tutoring in K-12 Education
Tushar Udeshi, Anna Khazenzon, Kabir Khan +5
Many AI tutors leverage large language models (LLMs) today. Given that LLMs are opaque black boxes, robust evaluation and live experimentation to measure the impact of every change…
Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning
Kaviraj Pather, Elena Hadjigeorgiou, Arben Krasniqi +4
Large language models (LLMs) show strong reasoning via chain-of-thought (CoT) prompting, but the process is opaque, which makes verification, debugging, and control difficult in hi…
Parameter-Efficient and Personalized Federated Training of Generative Models at the Edge
Kabir Khan, Manju Sarkar, Anita Kar +1
Large generative models (for example, language and diffusion models) enable high-quality text and image synthesis but are hard to train or adapt in cross-device federated settings…
Computational Economics in Large Language Models: Exploring Model Behavior and Incentive Design under Resource Constraints
Sandeep Reddy, Kabir Khan, Rohit Patil +5
Large language models (LLMs) are limited by substantial computational cost. We introduce a "computational economics" framework that treats an LLM as an internal economy of resource…
DySK-Attn: A Framework for Efficient, Real-Time Knowledge Updating in Large Language Models via Dynamic Sparse Knowledge Attention
Kabir Khan, Priya Sharma, Arjun Mehta +2
Large Language Models (LLMs) suffer from a critical limitation: their knowledge is static and quickly becomes outdated. Retraining these massive models is computationally prohibiti…