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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 1301–1325 of 2111 papers

TitleStatusHype
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information StructurizationCode2
Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented GenerationCode2
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users—0
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation—0
News Reporter: A Multi-lingual LLM Framework for Broadcast T.V News—0
KRAG Framework for Enhancing LLMs in the Legal Domain—0
TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked TextCode2
Context-Augmented Code Generation Using Programming Knowledge Graphs—0
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models—0
Improving Embedding Accuracy for Document Retrieval Using Entity Relationship Maps and Model-Aware Contrastive Sampling—0
ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception—0
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging—0
Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research—0
LightRAG: Simple and Fast Retrieval-Augmented GenerationCode14
LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge GraphsCode2
Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud—0
Exploring the Meaningfulness of Nearest Neighbor Search in High-Dimensional Space—0
Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG—0
Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQACode1
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation—0
GARLIC: LLM-Guided Dynamic Progress Control with Hierarchical Weighted Graph for Long Document QA—0
Deciphering the Interplay of Parametric and Non-parametric Memory in Retrieval-augmented Language ModelsCode0
TableRAG: Million-Token Table Understanding with Language ModelsCode0
Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in Medicine—0
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