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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 1201–1250 of 2111 papers

TitleStatusHype
CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models—0
PlanRAG: Planning-guided Retrieval Augmented Generation—0
Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language ModelsCode0
LLM Robustness Against Misinformation in Biomedical Question AnsweringCode0
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation—0
EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering—0
Mask-based Membership Inference Attacks for Retrieval-Augmented Generation—0
FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning—0
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems—0
An LLM Agent for Automatic Geospatial Data Analysis—0
PDL: A Declarative Prompt Programming LanguageCode3
Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models—0
Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation—0
LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor SearchCode2
LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question AnsweringCode2
Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination—0
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains—0
Graphusion: A RAG Framework for Knowledge Graph Construction with a Global PerspectiveCode1
An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A PlatformsCode0
SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback—0
DNAHLM -- DNA sequence and Human Language mixed large language ModelCode0
IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing—0
Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency—0
Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight—0
Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience ReportCode1
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance—0
RAC: Efficient LLM Factuality Correction with Retrieval AugmentationCode0
Who's Who: Large Language Models Meet Knowledge Conflicts in PracticeCode0
Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process—0
Leveraging Retrieval-Augmented Generation for Culturally Inclusive Hakka Chatbots: Design Insights and User Perceptions—0
ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation—0
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?—0
Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question CoverageCode1
BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via CompressionCode1
MMDS: A Multimodal Medical Diagnosis System Integrating Image Analysis and Knowledge-based Departmental Consultation—0
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs—0
Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot FrameworkCode9
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model—0
MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous VerificationCode0
AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA—0
Enhancing Retrieval Performance: An Ensemble Approach For Hard Negative Mining—0
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation—0
Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models—0
Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems—0
Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases—0
Real-time Fake News from Adversarial FeedbackCode0
ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope QuestionsCode0
SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques—0
Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks—0
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