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RAG

Retrieval-Augmented Generation (RAG) is a task that combines the strengths of both retrieval-based models and generation-based models. In this approach, a retrieval system selects relevant documents or passages from a large corpus, and a generation model, typically a neural language model, uses the retrieved information to generate a response. This method enhances the accuracy and coherence of generated text, especially in tasks requiring detailed knowledge or long context handling.

RAG is particularly useful in open-domain question answering, knowledge-grounded dialogue, and summarization tasks. The retrieval step helps the model to access and incorporate external information, making it less reliant on memorized knowledge and better suited for generating responses based on the latest or domain-specific information.

The performance of RAG systems is usually measured using metrics such as precision, recall, F1 score, BLEU score, and exact match. Some popular datasets for evaluating RAG models include Natural Questions, MS MARCO, TriviaQA, and SQuAD.

Papers

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Showing 551–575 of 2111 papers

TitleStatusHype
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation—0
A Survey on Knowledge-Oriented Retrieval-Augmented Generation—0
Continually Self-Improving Language Models for Bariatric Surgery Question--Answering—0
A Comparative Study of PDF Parsing Tools Across Diverse Document Categories—0
Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems—0
A Survey of Query Optimization in Large Language Models—0
Adaptive Retrieval-Augmented Generation for Conversational Systems—0
Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent Cluster Diagnosis System and Evaluation Framework—0
Context Tuning for Retrieval Augmented Generation—0
Context Embeddings for Efficient Answer Generation in RAG—0
Context Canvas: Enhancing Text-to-Image Diffusion Models with Knowledge Graph-Based RAG—0
A Survey of Multimodal Retrieval-Augmented Generation—0
A Graph-Retrieval-Augmented Generation Framework Enhances Decision-Making in the Circular Economy—0
Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model—0
Context-Augmented Code Generation Using Programming Knowledge Graphs—0
TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG—0
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation—0
Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation—0
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation—0
CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models—0
A Survey of Context Engineering for Large Language Models—0
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models—0
ConfusedPilot: Confused Deputy Risks in RAG-based LLMs—0
Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models—0
Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models—0
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