Papers with Code 아카이브
Retrieval
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- 평가표
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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/idea-iitd/greed
공식 코드GREED: A Neural Framework for Learning Graph Distance Functions
https://github.com/idea-iitd/neurosed
공식 코드GREED: A Neural Framework for Learning Graph Distance Functions
https://github.com/ielab/2dmse-reproduce
공식 코드https://github.com/ielab/agvaluate
공식 코드AgAsk: An Agent to Help Answer Farmer's Questions From Scientific Documents
https://github.com/ielab/apr
공식 코드https://github.com/ielab/asyncval
공식 코드Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training
https://github.com/ielab/boder
공식 코드Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
https://github.com/ielab/characterbert-dr
공식 코드https://github.com/ielab/counterfactual-dr
공식 코드Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach
https://github.com/ielab/dense-screening-feedback
공식 코드Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation
https://github.com/ielab/denseretriever-knowledge-acquisition
공식 코드Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition
https://github.com/ielab/dsr-poisoning
공식 코드Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks
https://github.com/ielab/green-ir
공식 코드Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models
https://github.com/ielab/llm-qlm
공식 코드Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
https://github.com/ielab/non-iid-foltr
공식 코드Is Non-IID Data a Threat in Federated Online Learning to Rank?
https://github.com/ielab/promptreps
공식 코드https://github.com/ielab/sysrev-seed-collection
공식 코드https://github.com/ielab/tilde
공식 코드Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion
https://github.com/ielab/typos-aware-bert
공식 코드Dealing with Typos for BERT-based Passage Retrieval and Ranking
https://github.com/ielab/understanding-splade
공식 코드https://github.com/ielab/vec2text-corpus-poisoning
공식 코드https://github.com/ielab/vec2text-dense_retriever-threat
공식 코드Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems
https://github.com/ielab/xqg4xdr
공식 코드Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval
https://github.com/iesl/CSFCube
공식 코드CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example
https://github.com/iesl/anncur
공식 코드Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
https://github.com/iesl/editable_user_profiles-lace
공식 코드https://github.com/iesl/knnlm-retrieval-quality
공식 코드You can't pick your neighbors, or can you? When and how to rely on retrieval in the $k$NN-LM
https://github.com/iesl/narrative-driven-rec-mint
공식 코드Large Language Model Augmented Narrative Driven Recommendations
https://github.com/ignorejjj/longrefiner
공식 코드Hierarchical Document Refinement for Long-context Retrieval-augmented Generation
https://github.com/ignorejjj/metarag
공식 코드https://github.com/iigroup/map
공식 코드MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
https://github.com/iigroup/scl
공식 코드Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning
https://github.com/iit-pavis/ganzzle
공식 코드GANzzle: Reframing jigsaw puzzle solving as a retrieval task using a generative mental image
https://github.com/iknoorjobs/multistage-bicross-encoder
공식 코드Multistage BiCross encoder for multilingual access to COVID-19 health information
https://github.com/ikuinen/CMIN
공식 코드Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos
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