https://github.com/songpipi/amdnet
공식 코드Towards Efficient Partially Relevant Video Retrieval with Active Moment Discovering
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 11
- 보존된 평가 기록
- 19
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/songw-sw/reknos
공식 코드Reasoning of Large Language Models over Knowledge Graphs with Super-Relations
https://github.com/sophiaalthammer/bert-pli
공식 코드Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility Study
https://github.com/sophiaalthammer/dossier_coliee
공식 코드https://github.com/sophiaalthammer/parm
공식 코드PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
https://github.com/sophiaalthammer/patent-lim
공식 코드Linguistically Informed Masking for Representation Learning in the Patent Domain
https://github.com/sophiaalthammer/tripjudge
공식 코드TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
https://github.com/soumickmj/micdir
공식 코드https://github.com/souravsaha/ir_explain
공식 코드https://github.com/souravsaha/qpp-comb
공식 코드Combining Query Performance Predictors: A Reproducibility Study
https://github.com/soyoung97/acurank
공식 코드AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking
https://github.com/soyoung97/listt5
공식 코드ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval
https://github.com/spackows/icaai-2024_rag-cd
공식 코드https://github.com/spandanagella/verse
공식 코드Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
https://github.com/spcl/mrag
공식 코드https://github.com/sphinxteam/PhaseRetrieval_demo
공식 코드Phase retrieval in high dimensions: Statistical and computational phase transitions
https://github.com/spotify-research/tot
공식 코드https://github.com/springcty/rag-fragility-to-linguistic-variation
공식 코드https://github.com/squeezeailab/tool2vec
공식 코드Efficient and Scalable Estimation of Tool Representations in Vector Space
https://github.com/sreyan88/recap
공식 코드https://github.com/sreyan88/reclap
공식 코드ReCLAP: Improving Zero Shot Audio Classification by Describing Sounds
https://github.com/srgrace/contextual-compression
공식 코드Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey
https://github.com/srhthu/lm-compeval-legal
공식 코드A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction
https://github.com/sshaar/That-is-a-Known-Lie
공식 코드That is a Known Lie: Detecting Previously Fact-Checked Claims
https://github.com/sshaar/clef2020-factchecking-task1
공식 코드Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media
https://github.com/sshaar/clef2020-factchecking-task2
공식 코드Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media
https://github.com/sshaar/clef2020-factchecking-task5
공식 코드Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media
https://github.com/ssm123ssm/docgpt-pharm
공식 코드https://github.com/sssohrab/PhDthesis
공식 코드Learning to compress and search visual data in large-scale systems
https://github.com/ssu-humane/hero
공식 코드HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims
https://github.com/stanford-futuredata/Baleen
공식 코드Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval
https://github.com/stanford-futuredata/ColBERT
공식 코드ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
https://github.com/stanford-futuredata/ares
공식 코드ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
https://github.com/stanford-oval/storm
공식 코드Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
https://github.com/stanford-oval/wikichat
공식 코드https://github.com/stanfordmimi/villa
공식 코드ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.