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Retrieval-augmented Generation

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Showing 1751–1775 of 2196 papers

TitleStatusHype
Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models—0
Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems—0
Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency—0
Optimizing Retrieval Augmented Generation for Object Constraint Language—0
Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning—0
Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning—0
ORANSight-2.0: Foundational LLMs for O-RAN—0
ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD—0
Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation—0
oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness—0
OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography—0
OSCAR: Online Soft Compression And Reranking—0
Osiris: A Lightweight Open-Source Hallucination Detection System—0
Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation—0
PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant—0
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research—0
Parallel Key-Value Cache Fusion for Position Invariant RAG—0
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion—0
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning—0
ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation—0
Passage Segmentation of Documents for Extractive Question Answering—0
Patchwork: A Unified Framework for RAG Serving—0
PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering—0
PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation—0
PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead—0
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