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 1751–1775 of 2111 papers

TitleStatusHype
Multi-Head RAG: Solving Multi-Aspect Problems with LLMsCode3
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential—0
Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices—0
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts—0
RATT: A Thought Structure for Coherent and Correct LLM ReasoningCode1
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks—0
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor—0
Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context UnderstandingCode0
UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models—0
Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin—0
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination—0
SoccerRAG: Multimodal Soccer Information Retrieval via Natural QueriesCode0
Demo: Soccer Information Retrieval via Natural Queries using SoccerRAGCode0
BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models—0
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation—0
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study—0
Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost—0
COS-Mix: Cosine Similarity and Distance Fusion for Improved Information RetrievalCode4
Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented GenerationCode0
RAG Does Not Work for Enterprises—0
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial TrainingCode1
Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning—0
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools—0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation—0
Show:102550
← PrevPage 71 of 85Next →

No leaderboard results yet.