no code implementations • 2 May 2024 • Elahe Khatibi, Mahyar Abbasian, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
This study not only shows the effectiveness of the ALCM but also underscores new research directions in leveraging the causal reasoning capabilities of LLMs.
no code implementations • 18 Feb 2024 • Zhongqi Yang, Elahe Khatibi, Nitish Nagesh, Mahyar Abbasian, Iman Azimi, Ramesh Jain, Amir M. Rahmani
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.
no code implementations • 15 Feb 2024 • Mahyar Abbasian, Zhongqi Yang, Elahe Khatibi, Pengfei Zhang, Nitish Nagesh, Iman Azimi, Ramesh Jain, Amir M. Rahmani
We compare the proposed CHA with GPT4.
1 code implementation • 3 Oct 2023 • Mahyar Abbasian, Iman Azimi, Amir M. Rahmani, Ramesh Jain
openCHA includes an orchestrator to plan and execute actions for gathering information from external sources, essential for formulating responses to user inquiries.
no code implementations • 21 Sep 2023 • Mahyar Abbasian, Elahe Khatibi, Iman Azimi, David Oniani, Zahra Shakeri Hossein Abad, Alexander Thieme, Ram Sriram, Zhongqi Yang, Yanshan Wang, Bryant Lin, Olivier Gevaert, Li-Jia Li, Ramesh Jain, Amir M. Rahmani
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.
no code implementations • 26 Jul 2023 • Mahyar Abbasian, Taha Rajabzadeh, Ahmadreza Moradipari, Seyed Amir Hossein Aqajari, HongSheng Lu, Amir Rahmani
Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets.