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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 1951–2000 of 2111 papers

TitleStatusHype
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation—0
RAM: Towards an Ever-Improving Memory System by Learning from Communications—0
RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation—0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models—0
iRAG: Advancing RAG for Videos with an Incremental Approach—0
Position Engineering: Boosting Large Language Models through Positional Information Manipulation—0
Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study—0
A Survey on Retrieval-Augmented Text Generation for Large Language Models—0
Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT—0
Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission—0
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision—0
Reducing hallucination in structured outputs via Retrieval-Augmented Generation—0
Generative Information Retrieval Evaluation—0
LLMs in Biomedicine: A study on clinical Named Entity RecognitionCode0
Onco-Retriever: Generative Classifier for Retrieval of EHR Records in Oncology—0
Towards Robustness of Text-to-Visualization Translation against Lexical and Phrasal Variability—0
Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform—0
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models—0
MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering—0
IITK at SemEval-2024 Task 2: Exploring the Capabilities of LLMs for Safe Biomedical Natural Language Inference for Clinical TrialsCode0
A Comparison of Methods for Evaluating Generative IRCode0
uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?Code0
Octopus v2: On-device language model for super agent—0
RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation—0
Observations on Building RAG Systems for Technical Documents—0
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs—0
Towards a Robust Retrieval-Based Summarization SystemCode0
Are Large Language Models Good at Utility Judgments?Code0
FACTOID: FACtual enTailment fOr hallucInation Detection—0
MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering—0
CPR: Retrieval Augmented Generation for Copyright Protection—0
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check—0
Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language—0
General LLMs as Instructors for Domain-Specific LLMs: A Sequential Fusion Method to Integrate Extraction and Editing—0
Improving Retrieval for RAG based Question Answering Models on Financial Documents—0
Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations—0
Towards a RAG-based Summarization Agent for the Electron-Ion ColliderCode0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systems—0
Improving Medical Multi-modal Contrastive Learning with Expert AnnotationsCode0
RAFT: Adapting Language Model to Domain Specific RAGCode0
Repoformer: Selective Retrieval for Repository-Level Code Completion—0
Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector—0
Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systemsCode0
AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production—0
Development of a Reliable and Accessible Caregiving Language Model (CaLM)—0
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design—0
HaluEval-Wild: Evaluating Hallucinations of Language Models in the WildCode0
FaaF: Facts as a Function for the evaluation of generated textCode0
Towards Comprehensive Vietnamese Retrieval-Augmented Generation and Large Language Models—0
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