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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/lucas-schwengber/h2q
공식 코드https://github.com/lucas-ventura/CoVR
공식 코드CoVR-2: Automatic Data Construction for Composed Video Retrieval
https://github.com/lucidrains/retrieval-augmented-ddpm
공식 코드https://github.com/luckyyysta/awesome-llm-hallucination
공식 코드https://github.com/ludanruan/clip4vla
공식 코드Accommodating Audio Modality in CLIP for Multimodal Processing
https://github.com/luffy06/refusion
공식 코드https://github.com/lugiavn/generalization-dml
공식 코드Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer?
https://github.com/lujunru/memochat
공식 코드MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
https://github.com/luka-group/FaviComp
공식 코드Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation
https://github.com/luka-group/ddr
공식 코드Dense Retrieval as Indirect Supervision for Large-space Decision Making
https://github.com/luka-group/mrcod
공식 코드Multi-hop Evidence Retrieval for Cross-document Relation Extraction
https://github.com/lukasbahr/kg-rag-fmea
공식 코드Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis
https://github.com/lukejaffe/gfn
공식 코드https://github.com/lukewys/chamber-ensemble-generator
공식 코드The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling
https://github.com/luminosityx/hat
공식 코드Unifying Two-Stream Encoders with Transformers for Cross-Modal Retrieval
https://github.com/lunarshen/dsicovla
공식 코드https://github.com/luomancs/remuq
공식 코드https://github.com/luomancs/retriever_reader_for_okvqa
공식 코드Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering
https://github.com/luowaterbi/tokenrecycling
공식 코드Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling
https://github.com/luoyan407/gradient_adjustment
공식 코드https://github.com/lupantech/promptpg
공식 코드RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
https://github.com/luyug/COIL
공식 코드COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
https://github.com/luyug/Condenser
공식 코드https://github.com/luyug/Reranker
공식 코드Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
https://github.com/lwgkzl/MedDG
공식 코드MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
https://github.com/lxchtan/tegtok
공식 코드TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
https://github.com/lxx909546478/QUSum
공식 코드Query-Utterance Attention with Joint modeling for Query-Focused Meeting Summarization
https://github.com/lyakaap/Landmark2019-1st-and-3rd-Place-Solution
공식 코드Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
https://github.com/lyan62/RobustCap
공식 코드Understanding Retrieval Robustness for Retrieval-Augmented Image Captioning
https://github.com/lygsbw/umg-clip
공식 코드UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
https://github.com/lynneeai/convosentinel
공식 코드Defending Against Social Engineering Attacks in the Age of LLMs
https://github.com/lyq312318224/mllms-augmented
공식 코드https://github.com/lyuchenyang/dialogue-to-video-retrieval
공식 코드https://github.com/lyutyuh/poptimizer
공식 코드Likelihood as a Performance Gauge for Retrieval-Augmented Generation
https://github.com/lzh-053/hmrn
공식 코드Hierarchical Matching and Reasoning for Multi-Query Image Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.