Innovative Approaches to Data Curation and Retrieval-Augmented Generation: From Annotation and Preparation to Retrieval
Abstract
This thesis investigates the challenges and advancements in data curation and Retrieval-Augmented Generation (RAG), addressing critical issues that impact the effectiveness of Large Language Models (LLMs). In recent years, RAG has garnered significant attention for its potential to reduce hallucination problems inherent in LLMs by integrating relevant external data sources. However, the success of RAG relies heavily on the quality of the curated data, which must undergo rigorous annotation, and preparation processes. ac The first work addresses the complexities of data annotation by presenting a novel cross-domain task allocation scheme to optimize annotator training and selection in crowdsourced scenarios. The second work shifts focus to data preparation, introducing an innovative framework for column semantic type annotation that considers inter-table context, thereby enhancing the effectiveness of data preparation. Finally, the third work explores the implementation of knowledge-based RAG approaches in the context of LLMs. We investigate strategies to effectively manage knowledge overloading and the complexities of multi-hop queries, developing a retrieval content summarization method tailored for knowledge base question answering. Collectively, these contributions aim to enhance the reliability and effectiveness of RAG for more robust AI systems capable of delivering accurate and contextually relevant responses. Through these contributions, this thesis not only…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.