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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 1476–1500 of 2111 papers

TitleStatusHype
Probing Causality Manipulation of Large Language Models—0
Claim Verification in the Age of Large Language Models: A Survey—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
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMsCode0
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction—0
GRATR: Zero-Shot Evidence Graph Retrieval-Augmented Trustworthiness ReasoningCode0
RAG-Optimized Tibetan Tourism LLMs: Enhancing Accuracy and Personalization—0
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented GenerationCode3
Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP StandardsCode1
Xinyu: An Efficient LLM-based System for Commentary Generation—0
WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain—0
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
Towardseffective teaching assistants: From intent-based chatbots to LLM-poweredteachingassistants—0
Reading with Intent—0
Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Research—0
Enhanced document retrieval with topic embeddings—0
Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation—0
LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal DomainCode2
Agentic Retrieval-Augmented Generation for Time Series Analysis—0
TC-RAG:Turing-Complete RAG's Case study on Medical LLM SystemsCode2
VERA: Validation and Evaluation of Retrieval-Augmented Systems—0
Meta Knowledge for Retrieval Augmented Large Language Models—0
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