Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data Augmentation
Fuente:
arXiv
Saved in:
| Main Authors: | Tamber, Manveer Singh, Kazi, Suleman, Sourabh, Vivek, Lin, Jimmy |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data
by: Tamber, Manveer Singh, et al.
Published: (2025)
by: Tamber, Manveer Singh, et al.
Published: (2025)
Illusions of Relevance: Arbitrary Content Injection Attacks Deceive Retrievers, Rerankers, and LLM Judges
by: Tamber, Manveer Singh, et al.
Published: (2025)
by: Tamber, Manveer Singh, et al.
Published: (2025)
Can't Hide Behind the API: Stealing Black-Box Commercial Embedding Models
by: Tamber, Manveer Singh, et al.
Published: (2024)
by: Tamber, Manveer Singh, et al.
Published: (2024)
Unifying Adversarial Robustness and Training Across Text Scoring Models
by: Tamber, Manveer Singh, et al.
Published: (2026)
by: Tamber, Manveer Singh, et al.
Published: (2026)
Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?
by: Lin, Jimmy
Published: (2024)
by: Lin, Jimmy
Published: (2024)
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers
by: Ma, Xueguang, et al.
Published: (2025)
by: Ma, Xueguang, et al.
Published: (2025)
Guiding Retrieval using LLM-based Listwise Rankers
by: Rathee, Mandeep, et al.
Published: (2025)
by: Rathee, Mandeep, et al.
Published: (2025)
An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking
by: Chen, Zijian, et al.
Published: (2024)
by: Chen, Zijian, et al.
Published: (2024)
UniRAG: Universal Retrieval Augmentation for Large Vision Language Models
by: Sharifymoghaddam, Sahel, et al.
Published: (2024)
by: Sharifymoghaddam, Sahel, et al.
Published: (2024)
Study on LLMs for Promptagator-Style Dense Retriever Training
by: Gwon, Daniel, et al.
Published: (2025)
by: Gwon, Daniel, et al.
Published: (2025)
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation
by: Chen, Haonan, et al.
Published: (2024)
by: Chen, Haonan, et al.
Published: (2024)
Listwise Generative Retrieval Models via a Sequential Learning Process
by: Tang, Yubao, et al.
Published: (2024)
by: Tang, Yubao, et al.
Published: (2024)
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
by: Zhuang, Shengyao, et al.
Published: (2024)
by: Zhuang, Shengyao, et al.
Published: (2024)
Negative Data Mining for Contrastive Learning in Dense Retrieval at IKEA.com
by: Agapaki, Eva, et al.
Published: (2026)
by: Agapaki, Eva, et al.
Published: (2026)
LACONIC: Dense-Level Effectiveness for Scalable Sparse Retrieval via a Two-Phase Training Curriculum
by: Xu, Zhichao, et al.
Published: (2026)
by: Xu, Zhichao, et al.
Published: (2026)
ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
by: Yao, Sijia, et al.
Published: (2025)
by: Yao, Sijia, et al.
Published: (2025)
A Survey of Model Architectures in Information Retrieval
by: Xu, Zhichao, et al.
Published: (2025)
by: Xu, Zhichao, et al.
Published: (2025)
Aligning Dense Retrievers with LLM Utility via Distillation
by: Sandhu, Rajinder, et al.
Published: (2026)
by: Sandhu, Rajinder, et al.
Published: (2026)
PairDistill: Pairwise Relevance Distillation for Dense Retrieval
by: Huang, Chao-Wei, et al.
Published: (2024)
by: Huang, Chao-Wei, et al.
Published: (2024)
LLM-based Listwise Reranking under the Effect of Positional Bias
by: Qiao, Jingfen, et al.
Published: (2026)
by: Qiao, Jingfen, et al.
Published: (2026)
VISA: Retrieval Augmented Generation with Visual Source Attribution
by: Ma, Xueguang, et al.
Published: (2024)
by: Ma, Xueguang, et al.
Published: (2024)
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval
by: Yoon, Soyoung, et al.
Published: (2024)
by: Yoon, Soyoung, et al.
Published: (2024)
Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track
by: Upadhyay, Shivani, et al.
Published: (2026)
by: Upadhyay, Shivani, et al.
Published: (2026)
RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation
by: Fu, Kairui, et al.
Published: (2026)
by: Fu, Kairui, et al.
Published: (2026)
On Listwise Reranking for Corpus Feedback
by: Yoon, Soyoung, et al.
Published: (2025)
by: Yoon, Soyoung, et al.
Published: (2025)
Rank-K: Test-Time Reasoning for Listwise Reranking
by: Yang, Eugene, et al.
Published: (2025)
by: Yang, Eugene, et al.
Published: (2025)
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank
by: Yu, Lulu, et al.
Published: (2024)
by: Yu, Lulu, et al.
Published: (2024)
Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking
by: Feng, Jun, et al.
Published: (2026)
by: Feng, Jun, et al.
Published: (2026)
Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation
by: Yang, Eugene, et al.
Published: (2024)
by: Yang, Eugene, et al.
Published: (2024)
Beyond Hard Negatives: The Importance of Score Distribution in Knowledge Distillation for Dense Retrieval
by: Jang, Youngjoon, et al.
Published: (2026)
by: Jang, Youngjoon, et al.
Published: (2026)
Options-Aware Dense Retrieval for Multiple-Choice query Answering
by: Singh, Manish, et al.
Published: (2025)
by: Singh, Manish, et al.
Published: (2025)
Boosting Data Utilization for Multilingual Dense Retrieval
by: Huang, Chao, et al.
Published: (2025)
by: Huang, Chao, et al.
Published: (2025)
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 Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval
by: Thakur, Nandan, et al.
Published: (2023)
by: Thakur, Nandan, et al.
Published: (2023)
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
by: Heuss, Maria, et al.
Published: (2024)
by: Heuss, Maria, et al.
Published: (2024)
AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation
by: Meng, Rui, et al.
Published: (2022)
by: Meng, Rui, et al.
Published: (2022)
A Survey on Retrieval-Augmented Text Generation for Large Language Models
by: Huang, Yizheng, et al.
Published: (2024)
by: Huang, Yizheng, et al.
Published: (2024)
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation
by: Wang, Shijie, et al.
Published: (2025)
by: Wang, Shijie, et al.
Published: (2025)
Zeroshot Listwise Learning to Rank Algorithm for Recommendation
by: Wang, Hao
Published: (2024)
by: Wang, Hao
Published: (2024)
When Vision Meets Texts in Listwise Reranking
by: Cai, Hongyi
Published: (2026)
by: Cai, Hongyi
Published: (2026)
Similar Items
-
Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data
by: Tamber, Manveer Singh, et al.
Published: (2025) -
Illusions of Relevance: Arbitrary Content Injection Attacks Deceive Retrievers, Rerankers, and LLM Judges
by: Tamber, Manveer Singh, et al.
Published: (2025) -
Can't Hide Behind the API: Stealing Black-Box Commercial Embedding Models
by: Tamber, Manveer Singh, et al.
Published: (2024) -
Unifying Adversarial Robustness and Training Across Text Scoring Models
by: Tamber, Manveer Singh, et al.
Published: (2026) -
Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?
by: Lin, Jimmy
Published: (2024)