Conversational AI
Conversational AI aims to create systems capable of engaging in natural, human-like dialogues, focusing on improving understanding, response generation, and safety. Current research heavily utilizes large language models (LLMs), often incorporating techniques like prompt engineering, fine-tuning, and multi-modal integration (combining text, images, and audio) to enhance performance and address limitations such as bias and factual inaccuracies. This field is significant due to its potential to revolutionize various sectors, including healthcare (e.g., patient engagement, risk assessment), education (e.g., tutoring systems), and creative industries (e.g., content generation, design assistance), while also raising crucial ethical considerations regarding transparency, safety, and bias mitigation.
Papers
Multimodal Contextual Dialogue Breakdown Detection for Conversational AI Models
Md Messal Monem Miah, Ulie Schnaithmann, Arushi Raghuvanshi, Youngseo Son
Graph Integrated Language Transformers for Next Action Prediction in Complex Phone Calls
Amin Hosseiny Marani, Ulie Schnaithmann, Youngseo Son, Akil Iyer, Manas Paldhe, Arushi Raghuvanshi
Toward Safe Evolution of Artificial Intelligence (AI) based Conversational Agents to Support Adolescent Mental and Sexual Health Knowledge Discovery
Jinkyung Park, Vivek Singh, Pamela Wisniewski
Token Trails: Navigating Contextual Depths in Conversational AI with ChatLLM
Md. Kowsher, Ritesh Panditi, Nusrat Jahan Prottasha, Prakash Bhat, Anupam Kumar Bairagi, Mohammad Shamsul Arefin