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/ukplab/arxiv2023-dapr
공식 코드https://github.com/ukplab/arxiv2024-attribute-or-abstain
공식 코드Attribute or Abstain: Large Language Models as Long Document Assistants
https://github.com/ukplab/gpl
공식 코드GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval
https://github.com/ukplab/incorporating-relevance
공식 코드https://github.com/ukplab/peerqa
공식 코드PeerQA: A Scientific Question Answering Dataset from Peer Reviews
https://github.com/ulab-uiuc/gor
공식 코드Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs
https://github.com/umanlp/ds-tod
공식 코드DS-TOD: Efficient Domain Specialization for Task Oriented Dialog
https://github.com/umanlp/multi2woz
공식 코드Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog
https://github.com/umanlp/zusammenqa
공식 코드https://github.com/umass-ml4ed/reticl
공식 코드RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
https://github.com/umass-ml4ed/syllabusqa
공식 코드https://github.com/umcu/negation-detection
공식 코드Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods
https://github.com/umr-ds/elastichash
공식 코드ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch
https://github.com/uniadapter/uniadapter
공식 코드UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
https://github.com/unicamp-dl/exaranker
공식 코드https://github.com/unicamp-dl/mMARCO
공식 코드mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset
https://github.com/unicamp-dl/neuclir22-mt5
공식 코드NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval
https://github.com/unum-cloud/usearch
공식 코드https://github.com/update-for-integrated-business-ai/amurd
공식 코드AMuRD: Annotated Arabic-English Receipt Dataset for Key Information Extraction and Classification
https://github.com/uridr/gtwiki
공식 코드A multi-task semi-supervised framework for Text2Graph & Graph2Text
https://github.com/usc-sail/mica-profession
공식 코드https://github.com/ushasi/CMIR-NET-A-deep-learning-based-model-for-cross-modal-retrieval-in-remote-sensing
공식 코드CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
https://github.com/ushasi/Earth-on-Canvas-dataset
공식 코드A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images
https://github.com/usnistgov/hilbert
공식 코드https://github.com/ustc-starteam/genki
공식 코드https://github.com/ustc-starteam/mirrn
공식 코드https://github.com/ustcagi/crag-in-kdd-cup2024
공식 코드https://github.com/ustcagi/pruningrag
공식 코드Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
https://github.com/ustcagi/rag-x
공식 코드Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
https://github.com/uta-smile/TCL
공식 코드Vision-Language Pre-Training with Triple Contrastive Learning
https://github.com/utahIRlab/AMTurk-Product-Search-Explanation-Evaluation
공식 코드Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
https://github.com/utahIRlab/drem-attention
공식 코드Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
https://github.com/utel-uiuc/gtfs_llm
공식 코드ChatGPT for GTFS: Benchmarking LLMs on GTFS Understanding and Retrieval
https://github.com/uvavision/DrillDown
공식 코드Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
https://github.com/uvavision/rerankingtransformer
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.