Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/huggingface/transformers/tree/master/examples/research_projects/rag-end2end-retriever
공식 코드Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering
https://github.com/hugochan/BAMnet
공식 코드Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
https://github.com/hugochan/KATE
공식 코드https://github.com/hugomcp/quadruplets
공식 코드https://github.com/hugothimonier/making-parametric-anomaly-detection-on-tabular-data-non-parametric-again
공식 코드https://github.com/hugothimonier/retrieval-augmented-deep-anomaly-detection-for-tabular-data
공식 코드https://github.com/hugozhl/mevi
공식 코드https://github.com/huichizhou/trustrag
공식 코드https://github.com/huiguanlab/dasd
공식 코드Dynamic Adapter with Semantics Disentangling for Cross-lingual Cross-modal Retrieval
https://github.com/huiguanlab/hico
공식 코드Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning
https://github.com/huiguanlab/nrccr
공식 코드Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning
https://github.com/huiguanlab/rpf
공식 코드https://github.com/huiminzeng/gpt-fedrec
공식 코드Federated Recommendation via Hybrid Retrieval Augmented Generation
https://github.com/hulu88/pmt
공식 코드https://github.com/hush-cd/HRDE
공식 코드https://github.com/huskyinsalt/crag
공식 코드https://github.com/husseinmozannar/SOQAL
공식 코드https://github.com/huxiaowan/sgmn
공식 코드Spatiotemporal Graph Guided Multi-modal Network for Livestreaming Product Retrieval
https://github.com/hwq0726/sefd
공식 코드SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text
https://github.com/hwy9855/gsr
공식 코드Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA
https://github.com/hyeon-jo/PSPNet
공식 코드Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video
https://github.com/hyeyoung1218/conquer
공식 코드https://github.com/hyintell/retrievalqa
공식 코드https://github.com/hymie122/rag-survey
공식 코드Retrieval-Augmented Generation for AI-Generated Content: A Survey
https://github.com/hyounghk/FixMyPose
공식 코드https://github.com/hyp1231/amazonreviews2023
공식 코드Bridging Language and Items for Retrieval and Recommendation
https://github.com/hyperconnect/corge
공식 코드Understanding and Improving the Exemplar-based Generation for Open-domain Conversation
https://github.com/hyperconnect/g2r
공식 코드https://github.com/hyperconnect/pseudo-dialog-prompting
공식 코드https://github.com/hyx1999/sam-decoding
공식 코드https://github.com/i-gao/adavision
공식 코드https://github.com/i3omar/ForestQB
공식 코드Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models
https://github.com/i3omar/LLM-Integration-Data
공식 코드Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models
https://github.com/iaar-shanghai/crud_rag
공식 코드https://github.com/iaar-shanghai/pgrag
공식 코드Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.