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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 1051–1075 of 2111 papers

TitleStatusHype
RankLLM: A Python Package for Reranking with LLMs—0
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs—0
RAR: Setting Knowledge Tripwires for Retrieval Augmented Rejection—0
RAVEN: Multitask Retrieval Augmented Vision-Language Learning—0
Re2G: Retrieve, Rerank, Generate—0
Reading with Intent—0
Reading with Intent -- Neutralizing Intent—0
RealDrive: Retrieval-Augmented Driving with Diffusion Models—0
REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark—0
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning—0
Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?—0
Real-time Spatial Retrieval Augmented Generation for Urban Environments—0
REAPER: Reasoning based Retrieval Planning for Complex RAG Systems—0
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
Reasoning LLMs for User-Aware Multimodal Conversational Agents—0
Attribution in Scientific Literature: New Benchmark and Methods—0
Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Research—0
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability—0
Reducing hallucination in structured outputs via Retrieval-Augmented Generation—0
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models—0
Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach—0
Re-identification of De-identified Documents with Autoregressive Infilling—0
Reinforcement Learning for Optimizing RAG for Domain Chatbots—0
REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing—0
Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks—0
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