Papers with Code 아카이브
Retrieval
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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/heydarsoudani/ragvsft
공식 코드Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge
https://github.com/hgissbkh/abstention-reranker
공식 코드Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism
https://github.com/hhansi/twitter-event-data-2019
공식 코드Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media
https://github.com/hhc1997/l2rm
공식 코드Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
https://github.com/hhc1997/mscn
공식 코드Noisy Correspondence Learning with Meta Similarity Correction
https://github.com/hhhuang/cag
공식 코드Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
https://github.com/hhy-huang/HiRAG
공식 코드https://github.com/hiba-meiruan/urdu-vqa-dataset-
공식 코드https://github.com/hibaahsan/chil_diagnosis_evidence
공식 코드Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges
https://github.com/hieunghia-pat/openvivqa-dataset
공식 코드OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese
https://github.com/hiro4bbh/sticker
공식 코드https://github.com/hitachinsk/fgt
공식 코드https://github.com/hitsz-tmg/read-and-select
공식 코드https://github.com/hitsz-tmg/sparse-retrieval-fewshot-el
공식 코드https://github.com/hj-ok/audiobert
공식 코드https://github.com/hkuds/lightrag
공식 코드https://github.com/hkuds/minirag
공식 코드MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
https://github.com/hkuds/videorag
공식 코드VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
https://github.com/hkunlp/icl-selective-annotation
공식 코드Selective Annotation Makes Language Models Better Few-Shot Learners
https://github.com/hkunlp/rsa
공식 코드Retrieved Sequence Augmentation for Protein Representation Learning
https://github.com/hkust-knowcomp/bekg
공식 코드https://github.com/hkust-knowcomp/privatengdb
공식 코드https://github.com/hkust-nlp/felm
공식 코드FELM: Benchmarking Factuality Evaluation of Large Language Models
https://github.com/hkust-vgd/lcd
공식 코드https://github.com/hlchen23/verified
공식 코드VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding
https://github.com/hlr/metarevision
공식 코드MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition
https://github.com/hltchkust/knowexpert
공식 코드Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
https://github.com/hltcoe/colbert-x
공식 코드Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models
https://github.com/hltcoe/hc4
공식 코드https://github.com/hltcoe/patapsco
공식 코드Patapasco: A Python Framework for Cross-Language Information Retrieval Experiments
https://github.com/hltcoe/peer_measure
공식 코드https://github.com/hltcoe/psq
공식 코드https://github.com/hltcoe/rank-k
공식 코드https://github.com/hly1998/nhl
공식 코드A Flexible Plug-and-Play Module for Generating Variable-Length
https://github.com/hlzz/mirror
공식 코드Matchable Image Retrieval by Learning from Surface Reconstruction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.