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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 1651–1675 of 2111 papers

TitleStatusHype
MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language ModelsCode0
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications—0
Conan-embedding: General Text Embedding with More and Better Negative Samples—0
Claim Verification in the Age of Large Language Models: A Survey—0
Probing Causality Manipulation of Large Language Models—0
Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models—0
Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering—0
Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language ModelsCode0
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction—0
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMsCode0
GRATR: Zero-Shot Evidence Graph Retrieval-Augmented Trustworthiness ReasoningCode0
A Quick, trustworthy spectral knowledge Q&A system leveraging retrieval-augmented generation on LLMCode0
Ancient Wisdom, Modern Tools: Exploring Retrieval-Augmented LLMs for Ancient Indian PhilosophyCode0
Xinyu: An Efficient LLM-based System for Commentary Generation—0
RAG-Optimized Tibetan Tourism LLMs: Enhancing Accuracy and Personalization—0
WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain—0
Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Research—0
Towardseffective teaching assistants: From intent-based chatbots to LLM-poweredteachingassistants—0
Reading with Intent—0
Enhanced document retrieval with topic embeddings—0
Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation—0
Agentic Retrieval-Augmented Generation for Time Series Analysis—0
Meta Knowledge for Retrieval Augmented Large Language Models—0
MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering—0
CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-CheckingCode0
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