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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/alfassy/FETA
공식 코드FETA: Towards Specializing Foundation Models for Expert Task Applications
https://github.com/algoprog/ProCIS
공식 코드ProCIS: A Benchmark for Proactive Retrieval in Conversations
https://github.com/ali-bahrainian/rag_best_practices
공식 코드Enhancing Retrieval-Augmented Generation: A Study of Best Practices
https://github.com/ali-vosoughi/counterfactual-audio
공식 코드Learning Audio Concepts from Counterfactual Natural Language
https://github.com/aliabdari/NLP_to_rank_artistic_Metaverses
공식 코드https://github.com/aliannejadi/Omicron
공식 코드https://github.com/aliannejadi/castur
공식 코드Harnessing Evolution of Multi-Turn Conversations for Effective Answer Retrieval
https://github.com/aliannejadi/qulac
공식 코드Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
https://github.com/alibaba-nlp/ibkd
공식 코드Text Representation Distillation via Information Bottleneck Principle
https://github.com/alibaba-nlp/masksearch
공식 코드MASKSEARCH: A Universal Pre-Training Framework to Enhance Agentic Search Capability
https://github.com/alibaba-nlp/multi-cpr
공식 코드Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval
https://github.com/alibaba-nlp/muver
공식 코드MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations
https://github.com/alibaba-nlp/omnisearch
공식 코드https://github.com/alibaba-nlp/rankinggpt
공식 코드A Two-Stage Adaptation of Large Language Models for Text Ranking
https://github.com/alibaba-nlp/vec-ra-odqa
공식 코드Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
https://github.com/alibaba-nlp/webwalker
공식 코드https://github.com/alibaba-nlp/zerosearch
공식 코드ZeroSearch: Incentivize the Search Capability of LLMs without Searching
https://github.com/alibaba/AliceMind
공식 코드VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation
https://github.com/alibaba/AliceMind/tree/main/mPLUG
공식 코드mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/alibaba/Deep-Vision/tree/main/Context-Motion-Decoupling
공식 코드Self-supervised Video Representation Learning by Context and Motion Decoupling
https://github.com/alibaba/EasyNLP
공식 코드DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding
https://github.com/alibaba/x-deeplearning
공식 코드https://github.com/alibabaresearch/damo-convai
공식 코드Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings
https://github.com/alicogintel/xdm
공식 코드XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System
https://github.com/alignmentresearch/gpt-4-novel-apis-attacks
공식 코드https://github.com/alinlab/s-clip
공식 코드S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
https://github.com/alipay/Ant-Multi-Modal-Framework
공식 코드Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
https://github.com/alipay/PainlessInferenceAcceleration
공식 코드https://github.com/alipay/agentuniverse
공식 코드PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
https://github.com/alirezamshi/AME-CMR
공식 코드Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task
https://github.com/alirezasalemi7/dedr-mm-fid
공식 코드https://github.com/alirezasalemi7/erag
공식 코드Evaluating Retrieval Quality in Retrieval-Augmented Generation
https://github.com/alirezasalemi7/pr-rag
공식 코드Plan-and-Refine: Diverse and Comprehensive Retrieval-Augmented Generation
https://github.com/alirezasalemi7/pretraining-multimodal-dense-retriever-for-okvqa
공식 코드Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering
https://github.com/alirezasalemi7/urag
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.