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/asrcompetition/content_modification_code
공식 코드Ranking-Incentivized Quality Preserving Content Modification
https://github.com/asrcompetition/content_modification_dataset
공식 코드Ranking-Incentivized Quality Preserving Content Modification
https://github.com/astroai-cfa/ariel_data_challenge_2023_solution
공식 코드https://github.com/asu-kim/personal-llm-kg
공식 코드Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph
https://github.com/asyml/forte
공식 코드https://github.com/at3e/lwgnet
공식 코드LWGNet: Learned Wirtinger Gradients for Fourier Ptychographic Phase Retrieval
https://github.com/atenanaz/poison-rag
공식 코드https://github.com/atomwiseinc/cslvae
공식 코드An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries
https://github.com/atonderski/lidarclip
공식 코드https://github.com/atosystem/speechclip
공식 코드SpeechCLIP: Integrating Speech with Pre-Trained Vision and Language Model
https://github.com/atri-acl/atri-acl
공식 코드https://github.com/atutej/RARe
공식 코드RARe: Retrieval Augmented Retrieval with In-Context Examples
https://github.com/augustinLib/SPIKE
공식 코드Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval
https://github.com/augustinlib/hype-why-these-documents
공식 코드Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths
https://github.com/autosurveys/autosurvey
공식 코드AutoSurvey: Large Language Models Can Automatically Write Surveys
https://github.com/avalab-gt/ctbls
공식 코드cTBLS: Augmenting Large Language Models with Conversational Tables
https://github.com/avariengien/causal-checker
공식 코드Look Before You Leap: A Universal Emergent Decomposition of Retrieval Tasks in Language Models
https://github.com/avikalp7/VQAtoCQA
공식 코드Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
https://github.com/avinashsai/ViRED
공식 코드https://github.com/avinashsai/mml
공식 코드https://github.com/awebson/congressional_adversary
공식 코드https://github.com/awesomehuang/bitcoin-gambling-dataset
공식 코드Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
https://github.com/awni00/dual-attention
공식 코드Disentangling and Integrating Relational and Sensory Information in Transformer Architectures
https://github.com/aws/selective-search-with-mutual-information-cotraining
공식 코드https://github.com/awslabs/decode-answer-logical-form
공식 코드DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
https://github.com/awslabs/graphrag-toolkit
공식 코드https://github.com/awslabs/neural-retrieval
공식 코드Embedding-based Zero-shot Retrieval through Query Generation
https://github.com/awslabs/rag-qa-arena
공식 코드RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
https://github.com/ayanami0730/deep_research_bench
공식 코드DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
https://github.com/ayankumarbhunia/adaptive-fgsbir
공식 코드https://github.com/ayankumarbhunia/stroke_subset_selector-for-fgsbir
공식 코드Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval
https://github.com/azgo14/classification_metric_learning
공식 코드Classification is a Strong Baseline for Deep Metric Learning
https://github.com/baai-dcai/m3d
공식 코드M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
https://github.com/baaivision/uni3d
공식 코드https://github.com/babangain/COLIEE2019
공식 코드IITP@COLIEE 2019: Legal Information Retrieval using BM25 and BERT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.