Papers with Code 아카이브
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/dmollaaliod/bioasq10b-public
공식 코드Query-focused Extractive Summarisation for Biomedical and COVID-19 Complex Question Answering
https://github.com/docailab/xrag
공식 코드https://github.com/dominic789654/longgenbench
공식 코드https://github.com/dongguanting/FollowRAG
공식 코드Toward General Instruction-Following Alignment for Retrieval-Augmented Generation
https://github.com/dongguanting/dpa-rag
공식 코드Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation
https://github.com/dongguanting/skp-for-kbqa
공식 코드Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA
https://github.com/donghwijung/gotloc
공식 코드GOTPR: General Outdoor Text-based Place Recognition Using Scene Graph Retrieval with OpenStreetMap
https://github.com/donglaiw/ibexHelper
공식 코드A Topological Nomenclature for 3D Shape Analysis in Connectomics
https://github.com/dongzhuoyao/video-query-in-the-wild
공식 코드https://github.com/donnotnormal/dcgh
공식 코드Deep Class-guided Hashing for Multi-label Cross-modal Retrieval
https://github.com/doodleui/wnd
공식 코드https://github.com/dopaminelcy/alta
공식 코드Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models
https://github.com/dosonleung/fasttog
공식 코드Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
https://github.com/doubling/Stochastic_Generative_Hashing
공식 코드https://github.com/dpappas/Data-Augmentation-for-Biomedical-Factoid-Question-Answering
공식 코드https://github.com/dps2022/diffusion-posterior-sampling
공식 코드Diffusion Posterior Sampling for General Noisy Inverse Problems
https://github.com/dr-lingxiao/ag-man
공식 코드Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval
https://github.com/dravenalg/oasis
공식 코드https://github.com/dreammr/rap
공식 코드Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG
https://github.com/dreasysnail/RetGen
공식 코드RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
https://github.com/dreichCSL/GQA_generalization_splits
공식 코드https://github.com/drogozhang/led
공식 코드LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval
https://github.com/drsy/motis
공식 코드Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval
https://github.com/drusso98/face-the-facts
공식 코드Face the Facts! Evaluating RAG-based Fact-checking Pipelines in Realistic Settings
https://github.com/dsaynova/pararel
공식 코드The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models
https://github.com/dsba-lab/rapid
공식 코드https://github.com/dsg-polymtl/flockmtl
공식 코드Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB
https://github.com/dthulke/dstc9-track1
공식 코드Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog
https://github.com/ducha-aiki/affnet
공식 코드Repeatability Is Not Enough: Learning Affine Regions via Discriminability
https://github.com/dugu9sword/LEMON
공식 코드Chinese Named Entity Recognition Augmented with Lexicon Memory
https://github.com/dun-research/3D-CSL
공식 코드3D-CSL: self-supervised 3D context similarity learning for Near-Duplicate Video Retrieval
https://github.com/dun-research/drca
공식 코드Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval
https://github.com/dundalia/twolar
공식 코드TWOLAR: a TWO-step LLM-Augmented distillation method for passage Reranking
https://github.com/duyali2000/mqmc
공식 코드Multi-queue Momentum Contrast for Microvideo-Product Retrieval
https://github.com/dvasilas/proteus
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.