Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 35 for “"Retrieval augmented generation (RAG)"”.
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AI Retrieval-Augmented Generation (RAG) System with Engineering Document Information Extraction for Employee Development
… model (LLM) support systems that utilize retrieval-augmented generation (RAG) to enhance employee effectiveness in trouble-shooting on a manufacturing production line. The RAG-augmented LLM tool is designed to ex-tract relevant data from proprietary engineering documents, including …
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QuOTE: Question-Oriented Text Embeddings
… Text Embeddings), a novel enhancement to retrieval- augmented generation (RAG) systems, aimed at improving document representation for accurate and nuanced retrieval. Unlike traditional RAG pipelines, which rely on embed- ding raw text chunks, QuOTE augments chunks with hypothetical …
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Augmenting large language models with static code analysis for accelerated software development and quality improvements
This thesis presents the Next-Generation Quality Accelerator (NGQA), an automated end-to-end pipeline driven by large language models (LLMs) to accelerate the quality assurance (QA) phase of the software development lifecycle using static analysis tools. NGQA integrates detection, grounding, …
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A Study on Deploying Large Language Models as Agents
… such as the context window and introduces Retrieval-Augmented Generation (RAG) as a solution to extend the model’s capability. Key APIs provided by OpenAI for deploying GPT models are discussed, highlighting their functionalities and applications. Finally, the practical application of LLMs …
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A Study on Leveraging Generative Artificial Intelligence and Text Clustering to Support Vendors
… on semantic similarity and on the employment of retrieval augmented generation (RAG) for extracting actionable insights. Our findings indicate a relative effectiveness of K-Means over DBSCAN in clustering feedback, but the overall effectiveness is moderate, which necessitates the need for human …
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CONDOR: Clinical Ontology-aware Networked Data Organization and Retrieval
… (SimPO). We compare a standard vector-based Retrieval-Augmented Generation (RAG) baseline against a more advanced GraphRAG architecture that leverages a two-tiered knowledge graph of patient data and medical ontologies. Our results demonstrate that the full CONDOR system, combining GraphRAG …
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ResearchBuddy AI: LLM-Powered Assistant
… of variable formats of GitHub READMEs, and Retrieval-Augmented Generation (RAG) for context-aware question answering over domain-specific documents. The research explains model selection through self-hosted open-source models with LoRA (Low Rank Adaptation) and other techniques to achieve …
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Privacy-Focused LLM for local data processing: Implementing OLLAMA and RAG to securely query personal files in closed environments
… operational efficiency. The system leverages OLLAMA pretrained models, fine-tuned using the Hugging- Face framework, to align with organizational workflows and domain-specific needs. A Retrieval-Augmented Generation (RAG) subsystem dynamically retrieves and processes internal document …
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Neural Document Segmentation Using Weighted Sliding Windows with Transformer Encoders
… (NLP), with notable applications in information retrieval and question answering. Effective text segmentation is crucial for enhancing Retrieval-Augmented Generation (RAG) systems by providing coherent segments that improve the contextual accuracy of responses. We introduce a weighted sliding …
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Seeing the Forest Through the Trees: Knowledge Retrieval for Streamlining Particle Physics Analysis
… to analyze openaccess data. Techniques such as Retrieval Augmented Generation (RAG) rely on semantically matching localized text chunks, but struggle to maintain coherent context when relevant information spans multiple segments, leading to a fragmented representation devoid of global …
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A retrieval augmented generation fine-tuned LLM model for refactored code recommendations to mitigate Java lock contention performance faults
… thesis presents a novel approach that combines Retrieval Augmented Generation (RAG) with a fine-tuned LLM model for refactored code recommendation aimed at reducing lock-contention performance faults in Java applications. The RAG-based fine-tuned model combines the strengths of contextual …
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Mediators: Participatory Collective Intelligence for Multi-Stakeholder Urban Decision-Making
… introduces a computational framework for AI-augmented collective decision-making in urban settings. Based on real-world case studies, the core decision-making process is abstracted as a multiplayer board game modeling the check-and-balance dynamics among stakeholders with differing values. …
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Disdoc: AI Teaching Assistant for Computer Science Courses
… connect the LLM to all course material through retrieval-augmented generation (RAG). To ensure the RAG system retrieves the most relevant information, we organize course material into question categories. We evaluated Disdoc in a research study on a 340-student Computer Systems class at Virginia …
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Integrating Open-Source Retrieval-Augmented Generation with Large Language Models for Business, Market and Responsibility Insights
… investigates the integration of open-source retrieval-augmented generation (RAG) with large language models (LLMs) on the Databricks platform. The aim is to provide advanced insights in the fields of business, market, and responsibility intelligence. The research explores combining RAG and …
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Improving Question Answering Systems with Retrieval Augmented Generation
… high computing resources. On the other hand, retrieval augmentation has been used to tackle knowledge-intensive tasks and proven by recent studies to be effective when coupled with LLMs. In this thesis, we explore Retrieval-Augmented Generation (RAG), a framework to augment generative LLMs …
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Essays on Data Frameworks and Sustainable AI for Public Health
… in Large Language Models (LLMs). It presents a Retrieval Augmented Generation (RAG) framework grounded in external sources of knowledge and enhanced by domain-specific prompt engineering for healthcare. To evaluate reliability, the Negative Missing Information Scoring System (NMISS) is …
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An end-to-end benchmarking framework for retrieval-augmented generation systems
… prototypes to production-grade services, Retrieval-Augmented Generation (RAG) has emerged as the de facto paradigm for mitigating hallucinations and incorporating up-to-date knowledge. While the accuracy of RAG systems has been extensively studied, the system performance—specifically …
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Policy-Based Access Control in Federated Clinical Question Answering
Retrieval augmented generation (RAG) has recently expanded large language model versatility in answering domain-specific questions using dynamic external knowledge bases, particularly demonstrating promise in assisting clinical settings. However, due to its sensitive nature, patient medical data …
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SPIRAL: Iterative Subgraph Expansion for Knowledge-Graph Based Retrieval-Augmented Generation
… prompt fails to anchor them to verifiable facts. Retrieval-augmented generation (RAG) mitigates this risk, yet existing graph-based retrievers either return bloated neighborhoods or incur prohibitive latency on large knowledge graphs (KGs). We introduce SPIRAL—Supervised Prior + Iterative …
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Enhancing Retrieval Augmented Generation Through Robust Information Retrieval
Retrieval-Augmented Generation (RAG) is a popular technique for grounding the responses of Large Language Models (LLMs). RAG works by extending Information Retrieval (IR) to incorporate LLMs that generate responses based on retrieved information and a query. RAG is commonly used within the field of …
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