https://github.com/AdeDZY/SIGIR19-BERT-IR
공식 코드Deeper Text Understanding for IR with Contextual Neural Language Modeling
Papers with Code 아카이브
Ad-Hoc Information Retrieval
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Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Georgetown-IR-Lab/cedr
공식 코드https://github.com/Georgetown-IR-Lab/contextualized-reps-for-ranking
공식 코드https://github.com/Georgetown-IR-Lab/multilingual-neural-ir
공식 코드Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
https://github.com/Georgetown-IR-Lab/prettr-neural-ir
공식 코드Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
https://github.com/Guzpenha/transformer_rankers
공식 코드On the Calibration and Uncertainty of Neural Learning to Rank Models
https://github.com/JorgeGabin/ColBERTKP
공식 코드https://github.com/NTMC-Community/MatchZoo
공식 코드https://github.com/OSU-slatelab/HARE
공식 코드HARE: a Flexible Highlighting Annotator for Ranking and Exploration
https://github.com/algoprog/InteRank
공식 코드Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking
https://github.com/cadobe/bison
공식 코드GLOW : Global Weighted Self-Attention Network for Web Search
https://github.com/canjiali/PARADE
공식 코드PARADE: Passage Representation Aggregation for Document Reranking
https://github.com/chainsawm/gccp
공식 코드Precise Zero-Shot Pointwise Ranking with LLMs through Post-Aggregated Global Context Information
https://github.com/cvangysel/cuNVSM
공식 코드https://github.com/cvangysel/sesh
공식 코드https://github.com/daod/coca
공식 코드Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
https://github.com/daod/dcl
공식 코드From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking
https://github.com/geek-ai/irgan
공식 코드IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
https://github.com/getalp/wikIR
공식 코드https://github.com/grill-lab/codec
공식 코드https://github.com/guyrosin/event_driven_qe
공식 코드https://github.com/hallogameboy/QDS-Transformer
공식 코드Long Document Ranking with Query-Directed Sparse Transformer
https://github.com/haon-chen/ase-official
공식 코드Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search
https://github.com/hltcoe/colbert-x
공식 코드Transfer Learning Approaches for Building Cross-Language Dense 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/llm-rankers
공식 코드A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
https://github.com/ielab/promptreps
공식 코드https://github.com/ielab/sdr
공식 코드Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study
https://github.com/isekulic/longformer-marco
공식 코드https://github.com/jingtaozhan/disentangled-retriever
공식 코드Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval
https://github.com/khui/copacrr
공식 코드Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
https://github.com/khui/repacrr
공식 코드Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
https://github.com/kkskp/re-rag
공식 코드https://github.com/krudra/qadocrank
공식 코드An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking
https://github.com/lcschv/toma
공식 코드https://github.com/liuqi6777/llm-relevance
공식 코드How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.