https://github.com/castorini/ragnarok
공식 코드Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track
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/castorini/rank_llm
공식 코드RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
https://github.com/castorini/touche-error-analysis
공식 코드https://github.com/castorini/umbrela
공식 코드UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance Assessor
https://github.com/castorini/unirag
공식 코드UniRAG: Universal Retrieval Augmentation for Large Vision Language Models
https://github.com/catherineschen/axiomatic-ir-interventions
공식 코드https://github.com/cau-se/gh-archive-code-retrieval
공식 코드JavaBERT: Training a transformer-based model for the Java programming language
https://github.com/cciiplab/persona_extend
공식 코드Improving Personality Consistency in Conversation by Persona Extending
https://github.com/cctien/bimultialign
공식 코드Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining
https://github.com/cdluminate/ladderloss
공식 코드https://github.com/celbree/reacc
공식 코드https://github.com/cesc47/eufcc-cir
공식 코드EUFCC-CIR: a Composed Image Retrieval Dataset for GLAM Collections
https://github.com/cevo-public/lapis
공식 코드LAPIS is a fast web API for massive open virus sequencing databases
https://github.com/cfm-uestc/DPQ
공식 코드Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval
https://github.com/cfm-uestc/DRQ
공식 코드Deep Recurrent Quantization for Generating Sequential Binary Codes
https://github.com/cfn-softbio/scibot
공식 코드https://github.com/cgartrel/nonsymmetric-DPP-learning
공식 코드https://github.com/cgcl-codes/advclip
공식 코드AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
https://github.com/cgcl-codes/badhash
공식 코드BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label
https://github.com/cgoemaere/hamdeq
공식 코드Accelerating Hopfield Network Dynamics: Beyond Synchronous Updates and Forward Euler
https://github.com/cgoemaere/hopdeq
공식 코드Accelerating Hopfield Network Dynamics: Beyond Synchronous Updates and Forward Euler
https://github.com/chandanbiswas08/infectracer
공식 코드https://github.com/chang-li/MergeDTS
공식 코드MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
https://github.com/changhongw/audio-embedding-bias
공식 코드Transfer Learning and Bias Correction with Pre-trained Audio Embeddings
https://github.com/chanliang/conner
공식 코드Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators
https://github.com/chanzuckerberg/MedMentions
공식 코드MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts
https://github.com/chao1224/chatdrug
공식 코드ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
https://github.com/chao1224/moleculestm
공식 코드Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
https://github.com/charles-ydd/trojanrag
공식 코드TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models
https://github.com/chatc/longdialsumm
공식 코드An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
https://github.com/cheerss/scipip
공식 코드https://github.com/chen700564/RGB
공식 코드Benchmarking Large Language Models in Retrieval-Augmented Generation
https://github.com/chendelong1999/remoteclip
공식 코드RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
https://github.com/chengzhipanpan/paser
공식 코드Sentence Representation Learning with Generative Objective rather than Contrastive Objective
https://github.com/chenlq123/MAE
공식 코드https://github.com/chenmingxiang110/AugNet
공식 코드AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.