https://github.com/amazon-science/neoqa
공식 코드NeoQA: Evidence-based Question Answering with Generated News Events
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/amazon-science/ragchecker
공식 코드RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
https://github.com/amazon-science/synthesizrr
공식 코드SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
https://github.com/amazonqa/amazonqa
공식 코드https://github.com/ambareeshravi/media_retrieval
공식 코드A multimodal deep learning framework for scalable content based visual media retrieval
https://github.com/aminvenv/lexica
공식 코드On the Interpolation of Contextualized Term-based Ranking with BM25 for Query-by-Example Retrieval
https://github.com/amirziai/rafic
공식 코드https://github.com/amsterdata/ragbooster
공식 코드Improving Retrieval-Augmented Large Language Models via Data Importance Learning
https://github.com/amy-hyunji/Generative-Multihop-Retrieval
공식 코드https://github.com/amy-hyunji/contextualized-generative-retrieval
공식 코드https://github.com/amy-hyunji/lora-for-retrieval
공식 코드Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders
https://github.com/amy-hyunji/routerretriever
공식 코드RouterRetriever: Routing over a Mixture of Expert Embedding Models
https://github.com/amzn/image-to-recipe-transformers
공식 코드Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning
https://github.com/amzn/pecos
공식 코드PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models
https://github.com/anastasiakrith/llm-for-vwsd
공식 코드Language Models as Knowledge Bases for Visual Word Sense Disambiguation
https://github.com/anastasiakrith/multimodal-retrieval-for-vwsd
공식 코드Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation
https://github.com/anaymehrotra/fairrankingwithnoisyattributes
공식 코드https://github.com/ancientshi/erm4
공식 코드https://github.com/ancientshi/personax
공식 코드PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence
https://github.com/andimarafioti/phaseRetrievalEvaluation
공식 코드Time-Frequency Phase Retrieval for Audio -- The Effect of Transform Parameters
https://github.com/andreamad8/FSB
공식 코드https://github.com/andreaschari/linguistic-transfer
공식 코드Improving Low-Resource Retrieval Effectiveness using Zero-Shot Linguistic Similarity Transfer
https://github.com/andreaschari/regional_spelling
공식 코드On the Effects of Regional Spelling Conventions in Retrieval Models
https://github.com/andrebola/icassp2021
공식 코드Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging
https://github.com/andreeaiana/nase
공식 코드https://github.com/andreineculai/mpc
공식 코드Probabilistic Compositional Embeddings for Multimodal Image Retrieval
https://github.com/andrewzhe/lawyer-llama
공식 코드https://github.com/angelvillar96/STLPose
공식 코드https://github.com/anguoyuan/pixel_retrieval-segmented_instance_retrieval
공식 코드Towards Content-based Pixel Retrieval in Revisited Oxford and Paris
https://github.com/angushuang17/corr_www
공식 코드https://github.com/aniksh/dual_encoder
공식 코드https://github.com/aniquetahir/JORA
공식 코드JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning
https://github.com/ankuPRK/COFC
공식 코드Dynamic Character Graph via Online Face Clustering for Movie Analysis
https://github.com/anlp-team/LTI_Neural_Navigator
공식 코드https://github.com/anmoln94/Team_LLM2KB_LM-KBC-2023
공식 코드LLM2KB: Constructing Knowledge Bases using instruction tuned context aware Large Language Models
https://github.com/anniejoan/uplifting-lower-income-data
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.