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Retrieval
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A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
MCoT-RE: Multi-Faceted Chain-of-Thought and Re-Ranking for Training-Free Zero-Shot Composed Image Retrieval
From Roots to Rewards: Dynamic Tree Reasoning with RL
HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals
A Survey of Context Engineering for Large Language Models
Context-Aware Search and Retrieval Over Erasure Channels
Language-Guided Contrastive Audio-Visual Masked Autoencoder with Automatically Generated Audio-Visual-Text Triplets from Videos
Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker
From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
Seq vs Seq: An Open Suite of Paired Encoders and Decoders
Kodezi Chronos: A Debugging-First Language Model for Repository-Scale, Memory-Driven Code Understanding
RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs
Temporal Information Retrieval via Time-Specifier Model Merging
Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation
Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation
Automatic Synthesis of High-Quality Triplet Data for Composed Image Retrieval
Differential Mamba
An analysis of vision-language models for fabric retrieval
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Model
LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering
LineRetriever: Planning-Aware Observation Reduction for Web Agents
RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
Dare to Plagiarize? Plagiarized Painting Recognition and Retrieval
Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval
ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
DAPFAM: A Domain-Aware Patent Retrieval Dataset Aggregated at the Family Level
JointRank: Rank Large Set with Single Pass
KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model
EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.