MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Fuente:
arXiv
Saved in:
| Main Authors: | Kalra, Jushaan Singh, Zhao, Xinran, Kim, To Eun, Cai, Fengyu, Diaz, Fernando, Wu, Tongshuang |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Revela: Dense Retriever Learning via Language Modeling
by: Cai, Fengyu, et al.
Published: (2025)
by: Cai, Fengyu, et al.
Published: (2025)
Sparse and Dense Retrievers Learn Better Together: Joint Sparse-Dense Optimization for Text-Image Retrieval
by: Song, Jonghyun, et al.
Published: (2025)
by: Song, Jonghyun, et al.
Published: (2025)
Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness
by: Zhao, Xinran, et al.
Published: (2024)
by: Zhao, Xinran, et al.
Published: (2024)
LTRR: Learning To Rank Retrievers for LLMs
by: Kim, To Eun, et al.
Published: (2025)
by: Kim, To Eun, et al.
Published: (2025)
The Wisdom of Many Queries: Complexity-Diversity Principle for Dense Retriever Training
by: Feng, Xincan, et al.
Published: (2026)
by: Feng, Xincan, et al.
Published: (2026)
Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval
by: Park, Seongwan, et al.
Published: (2025)
by: Park, Seongwan, et al.
Published: (2025)
$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
by: Cai, Fengyu, et al.
Published: (2024)
by: Cai, Fengyu, et al.
Published: (2024)
ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
by: Yao, Sijia, et al.
Published: (2025)
by: Yao, Sijia, et al.
Published: (2025)
Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation
by: Kim, To Eun, et al.
Published: (2024)
by: Kim, To Eun, et al.
Published: (2024)
AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment
by: Lai, Yilong, et al.
Published: (2024)
by: Lai, Yilong, et al.
Published: (2024)
Investigating Mixture of Experts in Dense Retrieval
by: Sokli, Effrosyni, et al.
Published: (2024)
by: Sokli, Effrosyni, 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)
Interpret and Control Dense Retrieval with Sparse Latent Features
by: Kang, Hao, et al.
Published: (2024)
by: Kang, Hao, et al.
Published: (2024)
TriSampler: A Better Negative Sampling Principle for Dense Retrieval
by: Yang, Zhen, et al.
Published: (2024)
by: Yang, Zhen, 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)
MoR: Mixture Of Representations For Mixed-Precision Training
by: Su, Bor-Yiing, et al.
Published: (2025)
by: Su, Bor-Yiing, et al.
Published: (2025)
cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree
by: Zhang, Yilin, et al.
Published: (2025)
by: Zhang, Yilin, et al.
Published: (2025)
Dense X Retrieval: What Retrieval Granularity Should We Use?
by: Chen, Tong, et al.
Published: (2023)
by: Chen, Tong, et al.
Published: (2023)
Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?
by: Lin, Jimmy
Published: (2024)
by: Lin, Jimmy
Published: (2024)
Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?
by: Zheng, JiaYing, et al.
Published: (2025)
by: Zheng, JiaYing, et al.
Published: (2025)
Tip of the Tongue Query Elicitation for Simulated Evaluation
by: He, Yifan, et al.
Published: (2025)
by: He, Yifan, et al.
Published: (2025)
Multilingual and Domain-Agnostic Tip-of-the-Tongue Query Generation for Simulated Evaluation
by: He, Xuhong, et al.
Published: (2026)
by: He, Xuhong, 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)
Learning to Select: Query-Aware Adaptive Dimension Selection for Dense Retrieval
by: Wu, Zhanyu, et al.
Published: (2026)
by: Wu, Zhanyu, et al.
Published: (2026)
Sparse Meets Dense: A Hybrid Approach to Enhance Scientific Document Retrieval
by: Mandikal, Priyanka, et al.
Published: (2024)
by: Mandikal, Priyanka, et al.
Published: (2024)
MoR: Mixture of Ranks for Low-Rank Adaptation Tuning
by: Tang, Chuanyu, et al.
Published: (2024)
by: Tang, Chuanyu, et al.
Published: (2024)
Query, Decompose, Compress: Structured Query Expansion for Efficient Multi-Hop Retrieval
by: Yun, JungMin, et al.
Published: (2026)
by: Yun, JungMin, et al.
Published: (2026)
Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
by: Zhang, Hengran, et al.
Published: (2025)
by: Zhang, Hengran, et al.
Published: (2025)
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)
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
by: Kim, To Eun, et al.
Published: (2024)
by: Kim, To Eun, et al.
Published: (2024)
RAQG-QPP: Query Performance Prediction with Retrieved Query Variants and Retrieval Augmented Query Generation
by: Tian, Fangzheng, et al.
Published: (2026)
by: Tian, Fangzheng, et al.
Published: (2026)
Optimizing Query Generation for Enhanced Document Retrieval in RAG
by: Koo, Hamin, et al.
Published: (2024)
by: Koo, Hamin, et al.
Published: (2024)
Mixture of Experts Approaches in Dense Retrieval Tasks
by: Sokli, Effrosyni, et al.
Published: (2025)
by: Sokli, Effrosyni, et al.
Published: (2025)
QueryBuilder: Human-in-the-Loop Query Development for Information Retrieval
by: Kandula, Hemanth, et al.
Published: (2024)
by: Kandula, Hemanth, et al.
Published: (2024)
Beyond Single Embeddings: Capturing Diverse Targets with Multi-Query Retrieval
by: Chen, Hung-Ting, et al.
Published: (2025)
by: Chen, Hung-Ting, et al.
Published: (2025)
A Gradient Accumulation Method for Dense Retriever under Memory Constraint
by: Kim, Jaehee, et al.
Published: (2024)
by: Kim, Jaehee, et al.
Published: (2024)
Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
by: Yang, Yuhao, et al.
Published: (2025)
by: Yang, Yuhao, et al.
Published: (2025)
Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data Augmentation
by: Tamber, Manveer Singh, et al.
Published: (2025)
by: Tamber, Manveer Singh, et al.
Published: (2025)
RaDeR: Reasoning-aware Dense Retrieval Models
by: Das, Debrup, et al.
Published: (2025)
by: Das, Debrup, et al.
Published: (2025)
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)
Similar Items
-
Revela: Dense Retriever Learning via Language Modeling
by: Cai, Fengyu, et al.
Published: (2025) -
Sparse and Dense Retrievers Learn Better Together: Joint Sparse-Dense Optimization for Text-Image Retrieval
by: Song, Jonghyun, et al.
Published: (2025) -
Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness
by: Zhao, Xinran, et al.
Published: (2024) -
LTRR: Learning To Rank Retrievers for LLMs
by: Kim, To Eun, et al.
Published: (2025) -
The Wisdom of Many Queries: Complexity-Diversity Principle for Dense Retriever Training
by: Feng, Xincan, et al.
Published: (2026)