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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 1551–1575 of 2111 papers

TitleStatusHype
How Much Can RAG Help the Reasoning of LLM?—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
Reward-RAG: Enhancing RAG with Reward Driven Supervision—0
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks—0
Enhancing Retrieval in QA Systems with Derived Feature AssociationCode0
Segment as You Wish -- Free-Form Language-Based Segmentation for Medical ImagesCode0
BordIRlines: A Dataset for Evaluating Cross-lingual Retrieval-Augmented GenerationCode0
Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis—0
Classifying Peace in Global Media Using RAG and Intergroup Reciprocity—0
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation—0
Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design PerspectiveCode0
LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and MitigationCode0
Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG—0
Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language ModelsCode0
BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain—0
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with FeedbackCode0
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation SystemsCode0
PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead—0
Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs—0
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations—0
AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow—0
A GEN AI Framework for Medical Note Generation—0
Corpus-informed Retrieval Augmented Generation of Clarifying Questions—0
Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation—0
Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation—0
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