Conversational Reply Generation For Customer Support Using Seq2Seq LLMs

This paper presented a Seq2Seq large language model framework for conversational reply generation in customer support systems. The proposed approach enhances response coherence, personalization, and scalability compared to traditional chatbot systems.   Future work includes integrating retrieval augmented generation for factual grounding, implementing continual learning from user feedback, incorporating multilingual conversational support, and improving hallucination detection mechanisms for safer deployment.   Advanced transformer-based LLMs can also be incorporated to generate more intelligent and personalized responses. Additional features such as voice-based communication, sentiment analysis, live database integration, and mobile application support can further improve the usability and performance of the system

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