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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

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Showing 626–650 of 2111 papers

TitleStatusHype
EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context—0
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation—0
Classifying Peace in Global Media Using RAG and Intergroup Reciprocity—0
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search—0
Claim Verification in the Age of Large Language Models: A Survey—0
A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences—0
Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm—0
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
A review of faithfulness metrics for hallucination assessment in Large Language Models—0
CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correction—0
A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science—0
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design—0
Evaluation of Attribution Bias in Retrieval-Augmented Large Language Models—0
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment—0
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle—0
Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain—0
Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation—0
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems—0
Evaluating the Retrieval Component in LLM-Based Question Answering Systems—0
CHORUS: Zero-shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Code—0
A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications—0
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models—0
A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models—0
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection—0
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods—0
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