Papers RAG
“RAG” 태그가 달린 논문 2,111편 · 필터 해제
A Survey of Context Engineering for Large Language Models
The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a formal discipline that transcends simple …
RAGRetrievalRetrieval-augmented GenerationSurveyDeveloping Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker
Traditional information extraction systems face challenges with text only language models as it does not consider infographics (visual elements of information) such as tables, charts, images etc. often used to convey com…
RAGRetrievalLeveraging RAG-LLMs for Urban Mobility Simulation and Analysis
With the rise of smart mobility and shared e-mobility services, numerous advanced technologies have been applied to this field. Cloud-based traffic simulation solutions have flourished, offering increasingly realistic re…
Decision MakingRAGMIRIX: Multi-Agent Memory System for LLM-Based Agents
Although memory capabilities of AI agents are gaining increasing attention, existing solutions remain fundamentally limited. Most rely on flat, narrowly scoped memory components, constraining their ability to personalize…
RAGCLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs
Large language models (LLMs), including zero-shot and few-shot paradigms, have shown promising capabilities in clinical text generation. However, real-world applications face two key challenges: (1) patient data is highl…
ChunkingRAGRetrievalRetrieval-augmented Generation+1Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation
Large language models (LLMs) incorporated with Retrieval-Augmented Generation (RAG) have demonstrated powerful capabilities in generating counterspeech against misinformation. However, current studies rely on limited evi…
InformativenessMisinformationRAGRetrieval+1The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover
The rapid adoption of Large Language Model (LLM) agents and multi-agent systems enables unprecedented capabilities in natural language processing and generation. However, these systems have introduced unprecedented secur…
Large Language ModelRAGOrchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning
Modern Artificial Intelligence (AI) increasingly relies on multi-agent architectures that blend visual and language understanding. Yet, a pressing challenge remains: How can we trust these agents especially in zero-shot …
BenchmarkingImage RetrievalOptical Character Recognition (OCR)RAG+3SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
Retrieval-augmented Generation (RAG) extends large language models (LLMs) with external knowledge but faces key challenges: restricted effective context length and redundancy in retrieved documents. Pure compression-base…
Evidence SelectionRAGRerankingRetrieval+4Flippi: End To End GenAI Assistant for E-Commerce
The emergence of conversational assistants has fundamentally reshaped user interactions with digital platforms. This paper introduces Flippi-a cutting-edge, end-to-end conversational assistant powered by large language m…
Intent Detectionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, encompassing how agents memorize, update, an…
Large Language ModelRAGRetrievalRetrieval-augmented GenerationAI-VaxGuide: An Agentic RAG-Based LLM for Vaccination Decisions
Vaccination plays a vital role in global public health, yet healthcare professionals often struggle to access immunization guidelines quickly and efficiently. National protocols and WHO recommendations are typically exte…
Question AnsweringRAGRetrieval-augmented GenerationKnowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work
The capabilities of Large Language Models (LLMs) have opened new frontiers for interacting with complex, domain-specific knowledge. However, prevailing methods like Retrieval-Augmented Generation (RAG) and general-purpos…
RAGRetrieval-augmented GenerationCyberRAG: An agentic RAG cyber attack classification and reporting tool
Intrusion Detection and Prevention Systems (IDS/IPS) in large enterprises can generate hundreds of thousands of alerts per hour, overwhelming security analysts with logs that demand deep, rapidly evolving domain expertis…
Intrusion DetectionRAGRetrieval-augmented GenerationRAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, while they remain prone to generating hallucinated or outdated responses due to their static internal knowledge. Recent advance…
Question AnsweringRAGReinforcement Learning (RL)Retrieval+1Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems
Large language models (LLMs) have recently been applied to dialog systems. Despite making progress, LLMs are prone to errors in knowledge-intensive scenarios. Recently, approaches based on retrieval augmented generation …
RAGResponse GenerationRetrieval-augmented GenerationARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RAG-based approaches often rely on static …
Large Language ModelNatural Language InferenceRAGRecommendation Systems+2Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
Existing research on Retrieval-Augmented Generation (RAG) primarily focuses on improving overall question-answering accuracy, often overlooking the quality of sub-claims within generated responses. Recent methods that at…
Conformal PredictionQuestion AnsweringRAGRetrieval+1PsyLite Technical Report
With the rapid development of digital technology, AI-driven psychological counseling has gradually become an important research direction in the field of mental health. However, existing models still have deficiencies in…
Large Language ModelLightweight DeploymentQuantizationRAGEraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typically assume a static corpus, requiring expe…
Graph ReconstructionRAGRetrievalRetrieval-augmented Generation