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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 1026–1050 of 2111 papers

TitleStatusHype
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately—0
Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
A New Pipeline For Generating Instruction Dataset via RAG and Self Fine-Tuning—0
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy—0
Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy—0
Advanced ingestion process powered by LLM parsing for RAG system—0
Enhancing Financial Time-Series Forecasting with Retrieval-Augmented Large Language Models—0
Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models—0
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation—0
LEANN: A Low-Storage Vector Index—0
100% Elimination of Hallucinations on RAGTruth for GPT-4 and GPT-3.5 Turbo—0
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning—0
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check—0
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation—0
A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking—0
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation—0
Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent Cluster Diagnosis System and Evaluation Framework—0
Blowfish: Topological and statistical signatures for quantifying ambiguity in semantic search—0
D2S-FLOW: Automated Parameter Extraction from Datasheets for SPICE Model Generation Using Large Language Models—0
Enhancing classroom teaching with LLMs and RAG—0
Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration—0
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation—0
An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought—0
Language Models "Grok" to Copy—0
Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models—0
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