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 1251–1275 of 2111 papers

TitleStatusHype
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsCode1
SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented GenerationCode0
Integrating Temporal Representations for Dynamic Memory Retrieval and Management in Large Language Models—0
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation SystemsCode1
HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings—0
GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models—0
How Does Knowledge Selection Help Retrieval Augmented Generation?—0
RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data RewardsCode2
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval—0
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models—0
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models—0
AT-RAG: An Adaptive RAG Model Enhancing Query Efficiency with Topic Filtering and Iterative ReasoningCode1
MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language ModelsCode3
Is Semantic Chunking Worth the Computational Cost?—0
Evaluation of Attribution Bias in Retrieval-Augmented Large Language Models—0
CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data DiversityCode1
Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical PerceptionCode3
RuleRAG: Rule-guided retrieval-augmented generation with language models for question answeringCode1
Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specifications—0
De-jargonizing Science for Journalists with GPT-4: A Pilot StudyCode0
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data—0
AIC CTU system at AVeriTeC: Re-framing automated fact-checking as a simple RAG taskCode0
SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented GenerationCode0
Self-adaptive Multimodal Retrieval-Augmented GenerationCode0
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability—0
Show:102550
← PrevPage 51 of 85Next →

No leaderboard results yet.