Image de couverture de livre

Layer-wise Token Compression for Efficient Document Reranking

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhuang, Shengyao, Xu, Zhichao, Lauriola, Ivano
Format:	Preprint
Publié:	2026
Sujets:	Information Retrieval
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Distillation versus Contrastive Learning: How to Train Your Rerankers
par: Xu, Zhichao, et autres
Publié: (2025)

Improving Document Retrieval Coherence for Semantically Equivalent Queries
par: Campese, Stefano, et autres
Publié: (2025)

Efficient Listwise Reranking with Compressed Document Representations
par: Déjean, Hervé, et autres
Publié: (2026)

Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning
par: Zhuang, Shengyao, et autres
Publié: (2025)

Reranking with Compressed Document Representation
par: Déjean, Hervé, et autres
Publié: (2025)

FIRST: Faster Improved Listwise Reranking with Single Token Decoding
par: Reddy, Revanth Gangi, et autres
Publié: (2024)

Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models
par: Li, Jieran, et autres
Publié: (2025)

State Space Models are Strong Text Rerankers
par: Xu, Zhichao, et autres
Publié: (2024)

A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
par: Zhuang, Shengyao, et autres
Publié: (2023)

EviRerank: Adaptive Evidence Construction for Long-Document LLM Reranking
par: Li, Minghan, et autres
Publié: (2024)

UniRank: Unified List-wise Reranking via Confidence-Ordered Denoising
par: Jia, Pengyue, et autres
Publié: (2026)

Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models
par: Zhuang, Shengyao, et autres
Publié: (2024)

Does Vec2Text Pose a New Corpus Poisoning Threat?
par: Zhuang, Shengyao, et autres
Publié: (2024)

PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
par: Zhuang, Shengyao, et autres
Publié: (2024)

Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks
par: Zhuang, Shengyao, et autres
Publié: (2025)

Rerank Before You Reason: Analyzing Reranking Tradeoffs through Effective Token Cost in Deep Search Agents
par: Sharifymoghaddam, Sahel, et autres
Publié: (2026)

Tevatron 2.0: Unified Document Retrieval Toolkit across Scale, Language, and Modality
par: Ma, Xueguang, et autres
Publié: (2025)

Leveraging LLMs for Unsupervised Dense Retriever Ranking
par: Khramtsova, Ekaterina, et autres
Publié: (2024)

Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation
par: Mao, Xinyu, et autres
Publié: (2024)

Efficient Long-Document Reranking via Block-Level Embeddings and Top-k Interaction Refinement
par: Li, Minghan, et autres
Publié: (2025)

Large Language Models for Stemming: Promises, Pitfalls and Failures
par: Wang, Shuai, et autres
Publié: (2024)

Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking
par: Huang, Jerry, et autres
Publié: (2025)

Where Relevance Emerges: A Layer-Wise Study of Internal Attention for Zero-Shot Re-Ranking
par: Chen, Haodong, et autres
Publié: (2026)

LACONIC: Dense-Level Effectiveness for Scalable Sparse Retrieval via a Two-Phase Training Curriculum
par: Xu, Zhichao, et autres
Publié: (2026)

Rethinking On-policy Optimization for Query Augmentation
par: Xu, Zhichao, et autres
Publié: (2025)

SumRank: Aligning Summarization Models for Long-Document Listwise Reranking
par: Feng, Jincheng, et autres
Publié: (2026)

ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval
par: Zheng, Yuanhang, et autres
Publié: (2024)

RankMamba: Benchmarking Mamba's Document Ranking Performance in the Era of Transformers
par: Xu, Zhichao
Publié: (2024)

Starbucks-v2: Improved Training for 2D Matryoshka Embeddings
par: Zhuang, Shengyao, et autres
Publié: (2024)

Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection
par: Khramtsova, Ekaterina, et autres
Publié: (2024)

Stop Overthinking: Unlocking Efficient Listwise Reranking with Minimal Reasoning
par: Liu, Danyang, et autres
Publié: (2026)

LURE-RAG: Lightweight Utility-driven Reranking for Efficient RAG
par: Chandra, Manish, et autres
Publié: (2026)

OSCAR: Online Soft Compression And Reranking
par: Louis, Maxime, et autres
Publié: (2025)

LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations
par: Gao, Jingtong, et autres
Publié: (2024)

GRADA: Graph-based Reranking against Adversarial Documents Attack
par: Zheng, Jingjie, et autres
Publié: (2025)

MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed
par: Zhan, Jiaqi Samantha, et autres
Publié: (2025)

An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision
par: Zhang, Zishuai, et autres
Publié: (2025)

Denoising Neural Reranker for Recommender Systems
par: Mao, Wenyu, et autres
Publié: (2025)

ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search
par: Wang, Shuai, et autres
Publié: (2024)

2D Matryoshka Training for Information Retrieval
par: Wang, Shuai, et autres
Publié: (2024)