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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 1826–1850 of 2111 papers

TitleStatusHype
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods—0
Evaluation of Attribution Bias in Retrieval-Augmented Large Language Models—0
Evaluation of RAG Metrics for Question Answering in the Telecom Domain—0
Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language—0
EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context—0
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment—0
Evidence Contextualization and Counterfactual Attribution for Conversational QA over Heterogeneous Data with RAG Systems—0
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
EvoPat: A Multi-LLM-based Patents Summarization and Analysis Agent—0
EvoWiki: Evaluating LLMs on Evolving Knowledge—0
Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software—0
ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses—0
Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models—0
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation—0
Exploiting the Layered Intrinsic Dimensionality of Deep Models for Practical Adversarial Training—0
Exploring Advanced Large Language Models with LLMsuite—0
Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overview—0
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems—0
Exploring Fact Memorization and Style Imitation in LLMs Using QLoRA: An Experimental Study and Quality Assessment Methods—0
Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment—0
Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector—0
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data—0
Exploring the Meaningfulness of Nearest Neighbor Search in High-Dimensional Space—0
Exploring the Potential of Large Language Models for Automation in Technical Customer Service—0
Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs—0
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