4 citations · 4 across the 5 of their papers we have counts for
5 papers
Investigating the Ability of Large Language Models to Analyze Recipes for Diabetes
Revathy Venkataramanan, Aditya Luthra, Venkatesan Nadimuthu +1
Several studies have evaluated the ability of Large Language Models (LLMs) for meal planning, yielding positive outcomes. These models can process natural language inputs and lever…
Neurosymbolic AI for Enhancing Instructability in Generative AI
Amit Sheth, Vishal Pallagani, Kaushik Roy
Generative AI, especially via Large Language Models (LLMs), has transformed content creation across text, images, and music, showcasing capabilities in following instructions throu…
Grounding from an AI and Cognitive Science Lens
Goonmeet Bajaj, Srinivasan Parthasarathy, Valerie L. Shalin +1
Grounding is a challenging problem, requiring a formal definition and different levels of abstraction. This article explores grounding from both cognitive science and machine learn…
A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression
Sumit Dalal, Deepa Tilwani, Kaushik Roy +4
The lack of explainability using relevant clinical knowledge hinders the adoption of Artificial Intelligence-powered analysis of unstructured clinical dialogue. A wealth of relevan…
Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety
Amit Sheth, Manas Gaur, Kaushik Roy +2
AI systems have been widely adopted across various domains in the real world. However, in high-value, sensitive, or safety-critical applications such as self-management for persona…