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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 1726–1750 of 2111 papers

TitleStatusHype
Current state of LLM Risks and AI Guardrails—0
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional InformationCode0
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL—0
ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate DisclosuresCode0
HIRO: Hierarchical Information Retrieval OptimizationCode0
We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMsCode1
Ad Auctions for LLMs via Retrieval Augmented Generation—0
Exploring Fact Memorization and Style Imitation in LLMs Using QLoRA: An Experimental Study and Quality Assessment Methods—0
Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning—0
Blowfish: Topological and statistical signatures for quantifying ambiguity in semantic search—0
Leveraging Large Language Models for Web Scraping—0
Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis—0
DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering—0
TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs—0
Scholarly Question Answering using Large Language Models in the NFDI4DataScience GatewayCode0
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs—0
Evaluating the Retrieval Component in LLM-Based Question Answering Systems—0
UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance AssessorCode2
Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for DialogueCode0
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents—0
A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback LearningCode3
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented GenerationCode0
Corpus Poisoning via Approximate Greedy Gradient DescentCode0
CRAG -- Comprehensive RAG BenchmarkCode3
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