SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1426–1450 of 2111 papers

TitleStatusHype
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models—0
Responsible Retrieval Augmented Generation for Climate Decision Making from Documents—0
Retrieval-Augmented Generation with Estimation of Source Reliability—0
Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations—0
Long^2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall—0
Semantic Enrichment of the Quantum Cascade Laser Properties in Text- A Knowledge Graph Generation ApproachCode0
Mind the Gap: A Generalized Approach for Cross-Modal Embedding Alignment—0
HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models—0
GraphAide: Advanced Graph-Assisted Query and Reasoning System—0
CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models—0
PlanRAG: Planning-guided Retrieval Augmented Generation—0
LLMs are Biased Evaluators But Not Biased for Retrieval Augmented GenerationCode0
Calibrated Decision-Making through LLM-Assisted Retrieval—0
Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language ModelsCode0
Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data—0
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation—0
LLM Robustness Against Misinformation in Biomedical Question AnsweringCode0
Mask-based Membership Inference Attacks for Retrieval-Augmented Generation—0
EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering—0
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems—0
FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning—0
An LLM Agent for Automatic Geospatial Data Analysis—0
Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation—0
Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models—0
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains—0
Show:102550
← PrevPage 58 of 85Next →

No leaderboard results yet.