Papers Retrieval-augmented Generation
“Retrieval-augmented Generation” 태그가 달린 논문 2,196편 · 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 GenerationSurveyContext-Aware Search and Retrieval Over Erasure Channels
This paper introduces and analyzes a search and retrieval model that adopts key semantic communication principles from retrieval-augmented generation. We specifically present an information-theoretic analysis of a remote…
DecoderRetrievalRetrieval-augmented GenerationSemantic CommunicationGraph World Model
World models (WMs) demonstrate strong capabilities in prediction, generation, and planning tasks. Existing WMs primarily focus on unstructured data and cannot leverage the ubiquitous structured data, often represented as…
Graph LearningmodelRetrieval-augmented GenerationCLI-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+1Orchestrator-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 GenerationJointRank: Rank Large Set with Single Pass
Efficiently ranking relevant items from large candidate pools is a cornerstone of modern information retrieval systems -- such as web search, recommendation, and retrieval-augmented generation. Listwise rerankers, which …
Information RetrievalRerankingRetrievalRetrieval-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+1EraRAG: 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 GenerationLeveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents. While RAG enhances large language model…
RAGRerankingRetrievalRetrieval-augmented GenerationLarge Language Models Acing Chartered Accountancy
Advanced intelligent systems, particularly Large Language Models (LLMs), are significantly reshaping financial practices through advancements in Natural Language Processing (NLP). However, the extent to which these model…
Legal ReasoningRetrieval-augmented Generation