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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/manestay/bordirlines
공식 코드BordIRlines: A Dataset for Evaluating Cross-lingual Retrieval-Augmented Generation
https://github.com/mangopy/direct-rag-learning
공식 코드Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
https://github.com/mangye16/DDAG
공식 코드Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
https://github.com/manishacharya60/rag-optimization
공식 코드Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation
https://github.com/manthan2305/Efficient-G-Retriever
공식 코드https://github.com/manveertamber/cadet-dense-retrieval
공식 코드https://github.com/manveertamber/content_injection_attacks
공식 코드https://github.com/manveertamber/enhancing_domain_adaptation
공식 코드Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data Augmentation
https://github.com/mao-ku/ems
공식 코드EMS: Efficient and Effective Massively Multilingual Sentence Embedding Learning
https://github.com/maple-research-lab/CLSA
공식 코드https://github.com/maps-research/gemrec
공식 코드https://github.com/marco-peer/hip23
공식 코드Feature Mixing for Writer Retrieval and Identification on Papyri Fragments
https://github.com/marco-peer/icdar23
공식 코드https://github.com/marco-peer/icdar24
공식 코드SAGHOG: Self-Supervised Autoencoder for Generating HOG Features for Writer Retrieval
https://github.com/marcosalfaro/tripletnetworksindoorlocalization
공식 코드Hierarchical localization with panoramic views and triplet loss functions
https://github.com/marcosfp97/llm-binary-health-qa
공식 코드Evaluating Search Engines and Large Language Models for Answering Health Questions
https://github.com/marios2019/CSN
공식 코드Cross-Shape Attention for Part Segmentation of 3D Point Clouds
https://github.com/mariyahendriksen/ecir2022_category_to_image_retrieval
공식 코드Extending CLIP for Category-to-image Retrieval in E-commerce
https://github.com/mariyahendriksen/ecir23-object-centric-vs-scene-centric-cmr
공식 코드Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
https://github.com/marker-inc-korea/autorag_aragog_paper
공식 코드AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline
https://github.com/markusfoote/mag1c
공식 코드https://github.com/marl/crepe
공식 코드https://github.com/marqo-ai/gcl
공식 코드Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
https://github.com/marsh312/dtr
공식 코드Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method
https://github.com/marslanm/multimodality-representation-learning
공식 코드Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
https://github.com/martiansideofthemoon/ai-detection-paraphrases
공식 코드Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
https://github.com/martiansideofthemoon/relic-retrieval
공식 코드https://github.com/martinetoering/ViCC
공식 코드Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
https://github.com/martius-lab/blackbox-backprop
공식 코드https://github.com/martius-lab/solver-differentiation-identity
공식 코드Backpropagation through Combinatorial Algorithms: Identity with Projection Works
https://github.com/maryeon/whiten_mtd
공식 코드Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval
https://github.com/marzenakrp/nocha
공식 코드One Thousand and One Pairs: A "novel" challenge for long-context language models
https://github.com/masakhane-io/afriqa
공식 코드AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages
https://github.com/masih4/MedImageRetrieval
공식 코드https://github.com/masoud-monajati/llm_bio_ner
공식 코드LLMs in Biomedicine: A study on clinical Named Entity Recognition
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