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Advanced Retrieval-Augmented Generation in Multi-Turn Dialogues and Heterogeneous Multi-Cloud Computing Environments
The Hong Kong University of Science and Technology Department of Computer Science and Engineering MPhil Thesis Defence Title: "Advanced Retrieval-Augmented Generation in Multi-Turn Dialogues and Heterogeneous Multi-Cloud Computing Environments" By Miss Feiyuan ZHANG Abstract: Traditional Retrieval-Augmented Generation (RAG) systems face significant limitations in multi-turn dialogue scenarios, failing to effectively capture and utilize dynamic conversational context. This thesis addresses both algorithmic innovations and distributed deployment challenges for advanced conversational AI systems. We introduce DH-RAG (Dynamic Historical Context-Powered Retrieval-Augmented Generation), a novel method specifically designed for multi-turn dialogues that integrates static external knowledge with dynamic conversational context. DH-RAG comprises a History-Learning based Query Reconstruction Module and a Dynamic History Information Updating Module, supported by a Dynamic Historical Information Database employing Historical Query Clustering, Hierarchical Matching, and Chain of Thought Tracking strategies. Experimental evaluation demonstrates that DH-RAG significantly outperforms conventional methods across multiple benchmarks, achieving improvements ranging from 25% to 85% in response quality metrics. On domain-specific datasets, DH-RAG achieves BLEU scores of 4.10 and F1 scores of 27.83, representing 215.38% and 58.13% improvements over baseline methods. Beyond algorithmic contributions, we design and implement a comprehensive distributed architecture for heterogeneous multi-cloud environments. Our dual- microservice architecture enables independent scaling while Our intelligent GPU scheduling system achieves 22% efficiency improvement and 18% cost optimization. The three-node deployment spanning Hong Kong, Shandong, and Jiangsu demonstrates 210% throughput improvement compared to single-machine deployment. This integrated approach validates that sophisticated conversational AI algorithms can be effectively deployed in distributed environments while maintaining algorithmic integrity and practical scalability, establishing a research paradigm bridging theoretical advances with practical engineering solutions. and other decentralized applications. Date: Friday, 1 August 2025 Time: 4:00pm - 6:00pm Venue: Room 3494 Lifts 25/26 Chairman: Prof. Song GUO Committee Members: Prof. Kai CHEN (Supervisor) Prof. Gary CHAN