Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/uwjunqi/Subrank
공식 코드Unsupervised Submodular Rank Aggregation on Score-based Permutations
https://github.com/uwnlp/piqa
공식 코드Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension
https://github.com/ux-decoder/find
공식 코드https://github.com/v-manhlt3/m-ltm-audio-text-retrieval
공식 코드Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation
https://github.com/v587su/nlqf
공식 코드On the Importance of Building High-quality Training Datasets for Neural Code Search
https://github.com/vacancy/NSCL-PyTorch-Release
공식 코드https://github.com/vahidaz/llamarec-lkg-rag
공식 코드LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking
https://github.com/vaishwarya96/hierarchy-image-retrieval
공식 코드Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval
https://github.com/val-iisc/sketch-parse
공식 코드https://github.com/valeoai/found
공식 코드Unsupervised Object Localization: Observing the Background to Discover Objects
https://github.com/vanh17/techqa
공식 코드Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading
https://github.com/varadhbhatnagar/fc-claim-det
공식 코드Harnessing Abstractive Summarization for Fact-Checked Claim Detection
https://github.com/varshakishore/incdsi
공식 코드https://github.com/vasiliskatr/faaf
공식 코드FaaF: Facts as a Function for the evaluation of generated text
https://github.com/vcm-project233/mitml
공식 코드https://github.com/vec-ai/wikihow-tiir
공식 코드https://github.com/vectara/faithbench
공식 코드FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
https://github.com/vectara/mirage-bench
공식 코드MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems
https://github.com/vectorinstitute/fed-rag
공식 코드FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems
https://github.com/vectorinstitute/naa
공식 코드https://github.com/veitelser/LWL
공식 코드https://github.com/vemvet/rotation-meanout-network
공식 코드A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval
https://github.com/venkteshv/dexter
공식 코드DEXTER: A Benchmark for open-domain Complex Question Answering using LLMs
https://github.com/verdurechen/sos-retrieval-loop
공식 코드https://github.com/verdurechen/understang_dsi
공식 코드https://github.com/verlab/DALF_CVPR_2023
공식 코드Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints
https://github.com/verlab/learningtodetect_prl_2023
공식 코드Improving the matching of deformable objects by learning to detect keypoints
https://github.com/verlab/learningtodetect_sibgrapi_2022
공식 코드Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images
https://github.com/verloop/few-shot-faqir
공식 코드Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ Retrieval
https://github.com/veronica320/wikihow-gosc
공식 코드https://github.com/veronicium/anchordr
공식 코드https://github.com/vicky-wil/remake
공식 코드https://github.com/victor-wang-902/surealm
공식 코드https://github.com/victordibia/neuralqa
공식 코드https://github.com/victorsungo/mmdialog
공식 코드MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.