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 1501–1525 of 2111 papers

TitleStatusHype
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability—0
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning—0
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG—0
Enhancing Retrieval-Augmented Audio Captioning with Generation-Assisted Multimodal Querying and Progressive Learning—0
Model-based Large Language Model Customization as Service—0
STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBack—0
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations—0
Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG—0
A Comparative Study of PDF Parsing Tools Across Diverse Document Categories—0
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning—0
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language ModelsCode0
Quebec Automobile Insurance Question-Answering With Retrieval-Augmented GenerationCode0
Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language ModelsCode0
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism—0
oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness—0
A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency ValidationCode0
News Reporter: A Multi-lingual LLM Framework for Broadcast T.V News—0
KRAG Framework for Enhancing LLMs in the Legal Domain—0
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation—0
No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models—0
Context-Augmented Code Generation Using Programming Knowledge Graphs—0
Exploring the Meaningfulness of Nearest Neighbor Search in High-Dimensional Space—0
Show:102550
← PrevPage 61 of 85Next →

No leaderboard results yet.