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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nikolamilosevic86/verif.ai
공식 코드https://github.com/nikosefth/freedom
공식 코드Composed Image Retrieval for Training-Free Domain Conversion
https://github.com/nil-zhuang/efficientrag-official
공식 코드EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
https://github.com/nilesh2797/dexml
공식 코드https://github.com/niluthpol/weak_supervised_video_moment
공식 코드https://github.com/ninatu/everything_at_once
공식 코드Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval
https://github.com/ninatu/in_style
공식 코드In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval
https://github.com/ningkko/umls_colloquial
공식 코드Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
https://github.com/ningkko/umls_colloquialism
공식 코드Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
https://github.com/nini-lxz/Rotation-Invariant-Point-Cloud-Analysis
공식 코드A Rotation-Invariant Framework for Deep Point Cloud Analysis
https://github.com/nisarg851/humberchatbot
공식 코드https://github.com/niuchuangnn/vllm_structure
공식 코드https://github.com/nivha/surface_networks_covers
공식 코드https://github.com/njtechcvlab/rstpreid-dataset
공식 코드DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval
https://github.com/nju-websoft/DraCo
공식 코드Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion
https://github.com/nju-websoft/KG2RAG
공식 코드https://github.com/nju-websoft/dyrren
공식 코드https://github.com/nju-websoft/epr-kgqa
공식 코드Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrieval
https://github.com/nju-websoft/formulaqa
공식 코드FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning
https://github.com/nju-websoft/formulareasoning
공식 코드FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning
https://github.com/nju-websoft/jeeves-gkmc
공식 코드When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
https://github.com/nju-websoft/mmrag
공식 코드https://github.com/njunlp/id-ockr
공식 코드Large Language Models are Limited in Out-of-Context Knowledge Reasoning
https://github.com/njust-vipgroup/semicon
공식 코드SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
https://github.com/nkandpa2/long_tail_knowledge
공식 코드https://github.com/nkrasner/cl-clip-align
공식 코드Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning
https://github.com/nla-group/fabba
공식 코드An efficient aggregation method for the symbolic representation of temporal data
https://github.com/nle-ml/mmkb
공식 코드Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
https://github.com/nle18/mice
공식 코드Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts
https://github.com/nlescoua/faiss-quickeradc
공식 코드Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMD
https://github.com/nlp-anonymous-researcher/legal-uqa
공식 코드LEGAL-UQA: A Low-Resource Urdu-English Dataset for Legal Question Answering
https://github.com/nlp-tlp/cleangraph
공식 코드CleanGraph: Human-in-the-loop Knowledge Graph Refinement and Completion
https://github.com/nlp2ct/hil-mt
공식 코드Human-in-the-loop Machine Translation with Large Language Model
https://github.com/nlpaueb/aueb-bioasq6
공식 코드https://github.com/nlpaueb/verify-refine-repass
공식 코드AUEB-Archimedes at RIRAG-2025: Is obligation concatenation really all you need?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.