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Found 7 papers, 1 papers with code
Empathy Through Multimodality in Conversational Interfaces
Agents represent one of the most emerging applications of Large Language Models (LLMs) and Generative AI, with their effectiveness hinging on multimodal capabilities to navigate complex user environments.
ALCM: Autonomous LLM-Augmented Causal Discovery Framework
This study not only shows the effectiveness of the ALCM but also underscores new research directions in leveraging the causal reasoning capabilities of LLMs.
ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework
The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content.
Knowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients
We compare the proposed CHA with GPT4.
Conversational Health Agents: A Personalized LLM-Powered Agent Framework
openCHA includes an orchestrator to plan and execute actions for gathering information from external sources, essential for formulating responses to user inquiries.
Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI
The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare.
Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation
Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets.
