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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/eleutherai/gpt-neox
공식 코드ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics
https://github.com/elias-ramzi/roadmap
공식 코드Robust and Decomposable Average Precision for Image Retrieval
https://github.com/elias-ramzi/suprank
공식 코드https://github.com/eliaslumer/graph-rag-tool-fusion-toollinkos
공식 코드https://github.com/elipei88/humberchatbotbackend
공식 코드https://github.com/eliphatfs/SkeletonMerger
공식 코드https://github.com/elki-project/elki
공식 코드ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"
https://github.com/ellenzhuwang/VK_OOD
공식 코드Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
https://github.com/elsa66666/moodangels
공식 코드A Retrieval-Augmented Multi-Agent Framework for Psychiatry Diagnosis
https://github.com/elviswxy/rag_fairness
공식 코드Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems
https://github.com/em-llm/EM-LLM-model
공식 코드https://github.com/emansim/text2image
공식 코드https://github.com/emarquer/morpho-analogy-amai
공식 코드Solving morphological analogies: from retrieval to generation
https://github.com/embedded-robotics/path-rag
공식 코드Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering
https://github.com/embeddings-benchmark/mteb
공식 코드https://github.com/embeddings-benchmark/mteb/blob/main/mteb/tasks/Retrieval/multilingual/CUREv1Retrieval.py
공식 코드CURE: A dataset for Clinical Understanding & Retrieval Evaluation
https://github.com/emerisly/edis
공식 코드EDIS: Entity-Driven Image Search over Multimodal Web Content
https://github.com/eminorhan/robust-vision
공식 코드Improving the robustness of ImageNet classifiers using elements of human visual cognition
https://github.com/emma1066/retrieval-augmented-it-openner
공식 코드Retrieval Augmented Instruction Tuning for Open NER with Large Language Models
https://github.com/emory-irlab/conqret-rag
공식 코드ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges
https://github.com/emory-irlab/query-explorer
공식 코드QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration
https://github.com/empathyang/jir-arena
공식 코드JIR-Arena: The First Benchmark Dataset for Just-in-time Information Recommendation
https://github.com/endrikacupaj/praline
공식 코드Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs
https://github.com/eoduself/udl
공식 코드Link, Synthesize, Retrieve: Universal Document Linking for Zero-Shot Information Retrieval
https://github.com/epfl-dlab/Cr5
공식 코드Crosslingual Document Embedding as Reduced-Rank Ridge Regression
https://github.com/epfl-dlab/llm-grounding-analysis
공식 코드A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia
https://github.com/epfl-lts2/mulan
공식 코드MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval
https://github.com/epfml/Bi-Sent2Vec
공식 코드https://github.com/epfml/landmark-attention
공식 코드Landmark Attention: Random-Access Infinite Context Length for Transformers
https://github.com/epic-kitchens/epic-kitchens-100-annotations
공식 코드https://github.com/epic-kitchens/epic-kitchens-100-narrator
공식 코드https://github.com/eqtpartners/companykg
공식 코드CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
https://github.com/ercong21/parc
공식 코드Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages
https://github.com/eric-ai-lab/comclip
공식 코드ComCLIP: Training-Free Compositional Image and Text Matching
https://github.com/erichson/superbench
공식 코드SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
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