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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 701–725 of 2111 papers

TitleStatusHype
Knowing When to Ask -- Bridging Large Language Models and Data—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks—0
A Proposal for Evaluating the Operational Risk for ChatBots based on Large Language Models—0
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model—0
Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets—0
Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large—0
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models—0
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models—0
Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond—0
Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety—0
Can Language Models Enable In-Context Database?—0
Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need—0
Evaluating the Effect of Retrieval Augmentation on Social Biases—0
Evaluating the Impact of Advanced LLM Techniques on AI-Lecture Tutors for a Robotics Course—0
Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain—0
Evaluating the Retrieval Component in LLM-Based Question Answering Systems—0
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
Evaluation of Attribution Bias in Retrieval-Augmented Large Language Models—0
Evaluation of RAG Metrics for Question Answering in the Telecom Domain—0
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging—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
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions—0
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