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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 1001–1025 of 2111 papers

TitleStatusHype
Improving Retrieval for RAG based Question Answering Models on Financial Documents—0
Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs—0
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering—0
Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification—0
Improving Zero-shot LLM Re-Ranker with Risk Minimization—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise—0
In-depth Analysis of Graph-based RAG in a Unified Framework—0
An LLM Agent for Automatic Geospatial Data Analysis—0
Inference Scaling for Bridging Retrieval and Augmented Generation—0
Inference Scaling for Long-Context Retrieval Augmented Generation—0
Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing—0
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
InfoTech Assistant : A Multimodal Conversational Agent for InfoTechnology Web Portal Queries—0
Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG—0
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism—0
In-Place Updates of a Graph Index for Streaming Approximate Nearest Neighbor Search—0
Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework—0
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation—0
Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation—0
Enhancing LLM Generation with Knowledge Hypergraph for Evidence-Based Medicine—0
A New Type of Foundation Model Based on Recordings of People's Emotions and Physiology—0
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