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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/alisafaya/neurocache
공식 코드Neurocache: Efficient Vector Retrieval for Long-range Language Modeling
https://github.com/alisonsneyd/poisson_stopping_method
공식 코드Modelling Stopping Criteria for Search Results using Poisson Processes
https://github.com/allegro/allRank
공식 코드Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention
https://github.com/allen-li1231/refiner-rag
공식 코드Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities
https://github.com/allen-li1231/treehop-rag
공식 코드TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering
https://github.com/allenai/ai2-scholarqa-lib
공식 코드Ai2 Scholar QA: Organized Literature Synthesis with Attribution
https://github.com/allenai/arc-solvers
공식 코드Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
https://github.com/allenai/cord19
공식 코드https://github.com/allenai/decomp
공식 코드Decomposed Prompting: A Modular Approach for Solving Complex Tasks
https://github.com/allenai/ir_datasets
공식 코드https://github.com/allenai/medicat
공식 코드MedICaT: A Dataset of Medical Images, Captions, and Textual References
https://github.com/allenai/qasc
공식 코드QASC: A Dataset for Question Answering via Sentence Composition
https://github.com/allenai/semanticilp
공식 코드Question Answering as Global Reasoning over Semantic Abstractions
https://github.com/allenai/topical
공식 코드https://github.com/allenschmaltz/memory_matching
공식 코드Coarse-to-Fine Memory Matching for Joint Retrieval and Classification
https://github.com/alon-albalak/xor-covid
공식 코드https://github.com/aloneirew/coresearch
공식 코드Cross-document Event Coreference Search: Task, Dataset and Modeling
https://github.com/alphafin-proj/alphafin
공식 코드AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework
https://github.com/alphaprime/target_attack_rank_aggregation
공식 코드https://github.com/alvinwan/neural-backed-decision-trees
공식 코드Dive into Decision Trees and Forests: A Theoretical Demonstration
https://github.com/am-azadi/SemEval2025-Task7-Word2winners
공식 코드Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
https://github.com/amaai-lab/jamendomaxcaps
공식 코드JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata
https://github.com/amaiya/onprem
공식 코드OnPrem.LLM: A Privacy-Conscious Document Intelligence Toolkit
https://github.com/amaralibey/bag-of-queries
공식 코드https://github.com/amaze18/RAGbot-OpenSource-Comparison/blob/main/Hybrid_Topk_similarity_expt.ipynb
공식 코드https://github.com/amazon-research/context-situated-pun-generation
공식 코드https://github.com/amazon-research/irgr
공식 코드Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
https://github.com/amazon-research/mix-generation
공식 코드https://github.com/amazon-research/video-contrastive-learning
공식 코드https://github.com/amazon-science/MAGID
공식 코드MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets
https://github.com/amazon-science/PrefEval
공식 코드Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
https://github.com/amazon-science/auto-rag-eval
공식 코드Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation
https://github.com/amazon-science/fq-bank
공식 코드https://github.com/amazon-science/llm-open-domain-table-reasoner
공식 코드OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
https://github.com/amazon-science/memerag
공식 코드MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.