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 2051–2075 of 2111 papers

TitleStatusHype
Bridging the Preference Gap between Retrievers and LLMs—0
Reinforcement Learning for Optimizing RAG for Domain Chatbots—0
Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models—0
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models—0
Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise—0
Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation—0
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods—0
A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models—0
Privacy-Preserved Neural Graph DatabasesCode0
ESGReveal: An LLM-based approach for extracting structured data from ESG reports—0
Efficient Title Reranker for Fast and Improved Knowledge-Intense NLP—0
Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge GapsCode0
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs—0
Context Tuning for Retrieval Augmented Generation—0
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research—0
Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool UseCode0
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions—0
SenTest: Evaluating Robustness of Sentence Encoders—0
How to Build an AI Tutor That Can Adapt to Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)—0
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model—0
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination—0
Minimizing Factual Inconsistency and Hallucination in Large Language Models—0
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis—0
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking—0
Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users—0
Show:102550
← PrevPage 83 of 85Next →

No leaderboard results yet.