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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 1751–1800 of 2111 papers

TitleStatusHype
Multi-Head RAG: Solving Multi-Aspect Problems with LLMsCode3
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential—0
Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices—0
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts—0
RATT: A Thought Structure for Coherent and Correct LLM ReasoningCode1
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks—0
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor—0
Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context UnderstandingCode0
UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models—0
Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin—0
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination—0
SoccerRAG: Multimodal Soccer Information Retrieval via Natural QueriesCode0
Demo: Soccer Information Retrieval via Natural Queries using SoccerRAGCode0
BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models—0
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation—0
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study—0
Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost—0
COS-Mix: Cosine Similarity and Distance Fusion for Improved Information RetrievalCode4
Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented GenerationCode0
RAG Does Not Work for Enterprises—0
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial TrainingCode1
Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning—0
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools—0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation—0
GNN-RAG: Graph Neural Retrieval for Large Language Model ReasoningCode3
One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language ModelsCode1
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts—0
Designing an Evaluation Framework for Large Language Models in Astronomy ResearchCode0
Toward Conversational Agents with Context and Time Sensitive Long-term MemoryCode1
Unlearning Climate Misinformation in Large Language Models—0
CtrlA: Adaptive Retrieval-Augmented Generation via Inherent ControlCode2
Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
A Multi-Source Retrieval Question Answering Framework Based on RAG—0
Don't Forget to Connect! Improving RAG with Graph-based Reranking—0
Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented GeneratorCode0
QUB-Cirdan at "Discharge Me!": Zero shot discharge letter generation by open-source LLM—0
EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling—0
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems—0
Augmenting Textual Generation via Topology Aware Retrieval—0
RAGSys: Item-Cold-Start Recommender as RAG System—0
Video Enriched Retrieval Augmented Generation Using Aligned Video CaptionsCode1
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-LearningCode2
Exploiting the Layered Intrinsic Dimensionality of Deep Models for Practical Adversarial Training—0
ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological TextCode1
CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge FusionCode9
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions—0
GRAG: Graph Retrieval-Augmented GenerationCode3
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