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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 701–750 of 2111 papers

TitleStatusHype
Are Large Language Models In-Context Graph Learners?—0
RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision—0
TrustRAG: An Information Assistant with Retrieval Augmented GenerationCode5
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering—0
What are Models Thinking about? Understanding Large Language Model Hallucinations "Psychology" through Model Inner State Analysis—0
PeerQA: A Scientific Question Answering Dataset from Peer ReviewsCode1
In-Place Updates of a Graph Index for Streaming Approximate Nearest Neighbor Search—0
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue—0
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation—0
PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational PathsCode3
Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models—0
Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing—0
MomentSeeker: A Task-Oriented Benchmark For Long-Video Moment Retrieval—0
Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation—0
Language Models are Few-Shot Graders—0
RAPID: Retrieval Augmented Training of Differentially Private Diffusion ModelsCode0
SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?—0
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge—0
REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark—0
SmartLLM: Smart Contract Auditing using Custom Generative AI—0
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models—0
Cognitive-Aligned Document Selection for Retrieval-augmented Generation—0
Revisiting Robust RAG: Do We Still Need Complex Robust Training in the Era of Powerful LLMs?—0
Does RAG Really Perform Bad For Long-Context Processing?—0
CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models—0
Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User ControlCode0
RAG vs. GraphRAG: A Systematic Evaluation and Key Insights—0
IterQR: An Iterative Framework for LLM-based Query Rewrite in e-Commercial Search System—0
Investigating Language Preference of Multilingual RAG Systems—0
Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs—0
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization—0
MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation—0
Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based TasksCode0
NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLMCode2
TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules—0
RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM GenerationCode2
QuOTE: Question-Oriented Text Embeddings—0
SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information—0
Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation—0
Gumbel Reranking: Differentiable End-to-End Reranker Optimization—0
CiteCheck: Towards Accurate Citation Faithfulness DetectionCode0
LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and RegularizationCode1
NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for Thai Legal Question AnsweringCode0
Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs—0
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation—0
MIR-Bench: Can Your LLM Recognize Complicated Patterns via Many-Shot In-Context Reasoning?—0
LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs - No Silver Bullet for LC or RAG RoutingCode0
Agentic Verification for Ambiguous Query Disambiguation—0
Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA—0
NeuroXVocal: Detection and Explanation of Alzheimer's Disease through Non-invasive Analysis of Picture-prompted Speech—0
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