Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 11
- 보존된 평가 기록
- 19
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/jingruihou/numur
공식 코드https://github.com/jingtaozhan/DRhard
공식 코드Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently
https://github.com/jingtaozhan/JPQ
공식 코드Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance
https://github.com/jingtaozhan/RepBERT-Index
공식 코드RepBERT: Contextualized Text Embeddings for First-Stage Retrieval
https://github.com/jingtaozhan/disentangled-retriever
공식 코드Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval
https://github.com/jingtaozhan/drscale
공식 코드https://github.com/jingtaozhan/extrapolate-eval
공식 코드Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
https://github.com/jingtaozhan/repconc
공식 코드https://github.com/jingxiaolun/tc-mgc
공식 코드TC-MGC: Text-Conditioned Multi-Grained Contrastive Learning for Text-Video Retrieval
https://github.com/jinhojsk515/ldmol
공식 코드LDMol: Text-to-Molecule Diffusion Model with Structurally Informative Latent Space
https://github.com/jinwon-ko/slcd
공식 코드https://github.com/jiny1623/dynamicer
공식 코드DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG
https://github.com/jinyangwu/NoiserBench
공식 코드https://github.com/jinyansu1/aggd
공식 코드https://github.com/jinyansu1/eval_poisonrag
공식 코드https://github.com/jinyansu1/flare-aug
공식 코드https://github.com/jinyugy21/adv-stickers_rhde
공식 코드Adversarial Sticker: A Stealthy Attack Method in the Physical World
https://github.com/jinzhuoran/rag-rewardbench
공식 코드https://github.com/jishengbai/audiosetcaps
공식 코드https://github.com/jiulongliu/MPRG
공식 코드https://github.com/jiwoongim/CCML
공식 코드https://github.com/jjmachan/DeepHash
공식 코드https://github.com/jl-wei/guing
공식 코드GUing: A Mobile GUI Search Engine using a Vision-Language Model
https://github.com/jlab-nlp/refpydst
공식 코드Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking
https://github.com/jlab-nlp/retrieval-augmented-correction
공식 코드RAC: Efficient LLM Factuality Correction with Retrieval Augmentation
https://github.com/jlibovicky/assess-multilingual-bert
공식 코드https://github.com/jliu-v/pk-ncli
공식 코드Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent
https://github.com/jlutangchuan/parts2words
공식 코드https://github.com/jmerullo/lm_vector_arithmetic
공식 코드Language Models Implement Simple Word2Vec-style Vector Arithmetic
https://github.com/jmnian/weak_label_for_rag
공식 코드W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering
https://github.com/jmnian/wrag
공식 코드W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering
https://github.com/jmorlana/dran
공식 코드Reuse your features: unifying retrieval and feature-metric alignment
https://github.com/joann8512/suspedal-gen
공식 코드https://github.com/joaodsmarques/lumberchunker
공식 코드https://github.com/joe817/rpt
공식 코드RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.