ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval
Fuente:
arXiv
Saved in:
| Main Authors: | Huang, Suyuan, Zhang, Chao, Wu, Yuanyuan, Zhang, Haoxin, Wang, Yuan, Wang, Maolin, Cao, Shaosheng, Xu, Tong, Zhao, Xiangyu, Qin, Zengchang, Gao, Yan, Bai, Yunhan, Fan, Jun, Hu, Yao, Chen, Enhong |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
NoteLLM: A Retrievable Large Language Model for Note Recommendation
by: Zhang, Chao, et al.
Published: (2024)
by: Zhang, Chao, et al.
Published: (2024)
On the Scaling of Robustness and Effectiveness in Dense Retrieval
by: Liu, Yu-An, et al.
Published: (2025)
by: Liu, Yu-An, et al.
Published: (2025)
NoteLLM-2: Multimodal Large Representation Models for Recommendation
by: Zhang, Chao, et al.
Published: (2024)
by: Zhang, Chao, et al.
Published: (2024)
TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework
by: Zhang, Chao, et al.
Published: (2025)
by: Zhang, Chao, et al.
Published: (2025)
Scaling Laws For Dense Retrieval
by: Fang, Yan, et al.
Published: (2024)
by: Fang, Yan, et al.
Published: (2024)
Scaling Sparse and Dense Retrieval in Decoder-Only LLMs
by: Zeng, Hansi, et al.
Published: (2025)
by: Zeng, Hansi, et al.
Published: (2025)
Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method
by: Liu, Ze, et al.
Published: (2024)
by: Liu, Ze, et al.
Published: (2024)
PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval
by: Lawrie, Dawn, et al.
Published: (2024)
by: Lawrie, Dawn, et al.
Published: (2024)
Does Generative Retrieval Overcome the Limitations of Dense Retrieval?
by: Zhang, Yingchen, et al.
Published: (2025)
by: Zhang, Yingchen, et al.
Published: (2025)
SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
by: Li, Xiaopeng, et al.
Published: (2024)
by: Li, Xiaopeng, et al.
Published: (2024)
Your Dense Retriever is Secretly an Expeditious Reasoner
by: Zhang, Yichi, et al.
Published: (2025)
by: Zhang, Yichi, et al.
Published: (2025)
A Simple and Effective Framework for Symmetric Consistent Indexing in Large-Scale Dense Retrieval
by: Wang, Huimu, et al.
Published: (2025)
by: Wang, Huimu, et al.
Published: (2025)
Training Dense Retrievers with Multiple Positive Passages
by: Wang, Benben, et al.
Published: (2026)
by: Wang, Benben, et al.
Published: (2026)
Dense X Retrieval: What Retrieval Granularity Should We Use?
by: Chen, Tong, et al.
Published: (2023)
by: Chen, Tong, et al.
Published: (2023)
Boosting Data Utilization for Multilingual Dense Retrieval
by: Huang, Chao, et al.
Published: (2025)
by: Huang, Chao, et al.
Published: (2025)
Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval
by: Zhang, Hanqi, et al.
Published: (2024)
by: Zhang, Hanqi, et al.
Published: (2024)
Xetrieval: Mechanistically Explaining Dense Retrieval
by: Cai, Zhixin, et al.
Published: (2026)
by: Cai, Zhixin, et al.
Published: (2026)
Generative Retrieval as Multi-Vector Dense Retrieval
by: Wu, Shiguang, et al.
Published: (2024)
by: Wu, Shiguang, et al.
Published: (2024)
Dense Passage Retrieval: Is it Retrieving?
by: Reichman, Benjamin, et al.
Published: (2024)
by: Reichman, Benjamin, et al.
Published: (2024)
Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
by: Fu, Dongqi, et al.
Published: (2026)
by: Fu, Dongqi, et al.
Published: (2026)
Event-enhanced Retrieval in Real-time Search
by: Zhang, Yanan, et al.
Published: (2024)
by: Zhang, Yanan, et al.
Published: (2024)
LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device Collaboration
by: Zhang, Yingyi, et al.
Published: (2025)
by: Zhang, Yingyi, et al.
Published: (2025)
Scaling Laws for Online Advertisement Retrieval
by: Wang, Yunli, et al.
Published: (2024)
by: Wang, Yunli, et al.
Published: (2024)
Exploring Training and Inference Scaling Laws in Generative Retrieval
by: Cai, Hongru, et al.
Published: (2025)
by: Cai, Hongru, et al.
Published: (2025)
Revela: Dense Retriever Learning via Language Modeling
by: Cai, Fengyu, et al.
Published: (2025)
by: Cai, Fengyu, et al.
Published: (2025)
Interpret and Control Dense Retrieval with Sparse Latent Features
by: Kang, Hao, et al.
Published: (2024)
by: Kang, Hao, et al.
Published: (2024)
PairDistill: Pairwise Relevance Distillation for Dense Retrieval
by: Huang, Chao-Wei, et al.
Published: (2024)
by: Huang, Chao-Wei, et al.
Published: (2024)
Cohort Retrieval using Dense Passage Retrieval
by: Jadhav, Pranav
Published: (2025)
by: Jadhav, Pranav
Published: (2025)
MRSE: An Efficient Multi-modality Retrieval System for Large Scale E-commerce
by: Jiang, Hao, et al.
Published: (2024)
by: Jiang, Hao, et al.
Published: (2024)
Generative Retrieval Overcomes Limitations of Dense Retrieval but Struggles with Identifier Ambiguity
by: Bracher, Adrian, et al.
Published: (2026)
by: Bracher, Adrian, et al.
Published: (2026)
Dense Passage Retrieval in Conversational Search
by: Salamah, Ahmed H., et al.
Published: (2025)
by: Salamah, Ahmed H., et al.
Published: (2025)
Domain Adaptation for Dense Retrieval and Conversational Dense Retrieval through Self-Supervision by Meticulous Pseudo-Relevance Labeling
by: Li, Minghan, et al.
Published: (2024)
by: Li, Minghan, et al.
Published: (2024)
LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval
by: Yang, Yingrui, et al.
Published: (2025)
by: Yang, Yingrui, et al.
Published: (2025)
DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning
by: Jiang, Pengcheng, et al.
Published: (2025)
by: Jiang, Pengcheng, et al.
Published: (2025)
Towards Dynamic Dense Retrieval with Routing Strategy
by: Su, Zhan, et al.
Published: (2026)
by: Su, Zhan, et al.
Published: (2026)
Generative Dense Retrieval: Memory Can Be a Burden
by: Yuan, Peiwen, et al.
Published: (2024)
by: Yuan, Peiwen, et al.
Published: (2024)
Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
by: Zhang, Yingyi, et al.
Published: (2025)
by: Zhang, Yingyi, et al.
Published: (2025)
Comparative Analysis of Retrieval Systems in the Real World
by: Mozolevskyi, Dmytro, et al.
Published: (2024)
by: Mozolevskyi, Dmytro, et al.
Published: (2024)
HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation
by: Madhu, Hiren, et al.
Published: (2026)
by: Madhu, Hiren, et al.
Published: (2026)
Leveraging LLMs for Unsupervised Dense Retriever Ranking
by: Khramtsova, Ekaterina, et al.
Published: (2024)
by: Khramtsova, Ekaterina, et al.
Published: (2024)
Similar Items
-
NoteLLM: A Retrievable Large Language Model for Note Recommendation
by: Zhang, Chao, et al.
Published: (2024) -
On the Scaling of Robustness and Effectiveness in Dense Retrieval
by: Liu, Yu-An, et al.
Published: (2025) -
NoteLLM-2: Multimodal Large Representation Models for Recommendation
by: Zhang, Chao, et al.
Published: (2024) -
TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework
by: Zhang, Chao, et al.
Published: (2025) -
Scaling Laws For Dense Retrieval
by: Fang, Yan, et al.
Published: (2024)