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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 1526–1550 of 2111 papers

TitleStatusHype
Augmenting Textual Generation via Topology Aware Retrieval—0
AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA—0
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation—0
Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models—0
Automated Code Generation and Validation for Software Components of Microcontrollers—0
Automated Conversion of Static to Dynamic Scheduler via Natural Language—0
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution—0
Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation—0
Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search—0
Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks—0
Automatic Teaching Platform on Vision Language Retrieval Augmented Generation—0
Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research—0
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesis—0
AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs—0
AviationLLM: An LLM-based Knowledge System for Aviation Training—0
A Visual RAG Pipeline for Few-Shot Fine-Grained Product Classification—0
Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models—0
BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models—0
BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text—0
Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications—0
Balancing Content Size in RAG-Text2SQL System—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation—0
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