Cover Image

Interpret and Control Dense Retrieval with Sparse Latent Features

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kang, Hao, Wang, Tevin, Xiong, Chenyan
Format:	Preprint
Published:	2024
Subjects:	Information Retrieval
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enhancing Dense Retrievers' Robustness with Group-level Reweighting
by: Han, Peixuan, et al.
Published: (2023)

Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval
by: Park, Seongwan, et al.
Published: (2025)

ResearchArena: Benchmarking Large Language Models' Ability to Collect and Organize Information as Research Agents
by: Kang, Hao, et al.
Published: (2024)

Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval
by: Coelho, João, et al.
Published: (2024)

MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin
by: Zhou, Tianshuo, et al.
Published: (2023)

Scaling Sparse and Dense Retrieval in Decoder-Only LLMs
by: Zeng, Hansi, et al.
Published: (2025)

Improve Dense Passage Retrieval with Entailment Tuning
by: Dai, Lu, et al.
Published: (2024)

LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval
by: Yang, Yingrui, et al.
Published: (2025)

Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?
by: Lin, Jimmy
Published: (2024)

Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
by: Yu, Shi, et al.
Published: (2023)

Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding
by: Cheng, Yiruo, et al.
Published: (2024)

IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation
by: Saxena, Yash, et al.
Published: (2026)

Interpretability Analysis of Domain Adapted Dense Retrievers
by: Yuksel, Goksenin, et al.
Published: (2025)

Sparse Meets Dense: A Hybrid Approach to Enhance Scientific Document Retrieval
by: Mandikal, Priyanka, et al.
Published: (2024)

Sparse and Dense Retrievers Learn Better Together: Joint Sparse-Dense Optimization for Text-Image Retrieval
by: Song, Jonghyun, et al.
Published: (2025)

A Gradient Accumulation Method for Dense Retriever under Memory Constraint
by: Kim, Jaehee, et al.
Published: (2024)

Efficient and Effective Retrieval of Dense-Sparse Hybrid Vectors using Graph-based Approximate Nearest Neighbor Search
by: Zhang, Haoyu, et al.
Published: (2024)

LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval
by: Jin, Jiajie, et al.
Published: (2026)

Aligning Web Query Generation with Ranking Objectives via Direct Preference Optimization
by: Coelho, João, et al.
Published: (2025)

What Generative Search Engines Like and How to Optimize Web Content Cooperatively
by: Wu, Yujiang, et al.
Published: (2025)

Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval
by: Lee, Sangam, et al.
Published: (2025)

PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
by: Zhuang, Shengyao, et al.
Published: (2024)

Training Dense Retrievers with Multiple Positive Passages
by: Wang, Benben, et al.
Published: (2026)

SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
by: Li, Xiaopeng, et al.
Published: (2024)

Does Generative Retrieval Overcome the Limitations of Dense Retrieval?
by: Zhang, Yingchen, et al.
Published: (2025)

Boosting Data Utilization for Multilingual Dense Retrieval
by: Huang, Chao, et al.
Published: (2025)

Dense Passage Retrieval in Conversational Search
by: Salamah, Ahmed H., et al.
Published: (2025)

On the Scaling of Robustness and Effectiveness in Dense Retrieval
by: Liu, Yu-An, 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)

Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval
by: Zhang, Hanqi, et al.
Published: (2024)

Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
by: Yang, Yuhao, et al.
Published: (2025)

Dense Passage Retrieval: Is it Retrieving?
by: Reichman, Benjamin, et al.
Published: (2024)

Bridging Dense and Sparse Maximum Inner Product Search
by: Bruch, Sebastian, et al.
Published: (2023)

Generative Retrieval Overcomes Limitations of Dense Retrieval but Struggles with Identifier Ambiguity
by: Bracher, Adrian, et al.
Published: (2026)

Leveraging LLMs for Unsupervised Dense Retriever Ranking
by: Khramtsova, Ekaterina, et al.
Published: (2024)

DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense Retrieval
by: Mao, Xinyu, et al.
Published: (2025)

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval
by: Huang, Suyuan, et al.
Published: (2024)

AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment
by: Lai, Yilong, et al.
Published: (2024)

Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection
by: Khramtsova, Ekaterina, et al.
Published: (2024)

Static Pruning in Dense Retrieval using Matrix Decomposition
by: Siciliano, Federico, et al.
Published: (2024)