Image de couverture de livre

A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhuang, Shengyao, Zhuang, Honglei, Koopman, Bevan, Zuccon, Guido
Format:	Preprint
Publié:	2023
Sujets:	Information Retrieval Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

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

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)

Zero-shot Generative Large Language Models for Systematic Review Screening Automation
par: Wang, Shuai, 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)

LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback
par: Li, Hang, et autres
Publié: (2025)

Whole-Pool Setwise Reranking with Long-Context Language Models
par: Li, Hang, et autres
Publié: (2026)

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

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

Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems
par: Zhuang, Shengyao, et autres
Publié: (2024)

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

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

Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking
par: Schlatt, Ferdinand, et autres
Publié: (2024)

VISA: Retrieval Augmented Generation with Visual Source Attribution
par: Ma, Xueguang, et autres
Publié: (2024)

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

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

An Investigation of Prompt Variations for Zero-shot LLM-based Rankers
par: Sun, Shuoqi, et autres
Publié: (2024)

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

Beyond Reproducibility: Advancing Zero-shot LLM Reranking Efficiency with Setwise Insertion
par: Podolak, Jakub, et autres
Publié: (2025)

TPRF: A Transformer-based Pseudo-Relevance Feedback Model for Efficient and Effective Retrieval
par: Li, Hang, et autres
Publié: (2024)

On the impact of retrieved content representations in RAG Pipelines
par: Ross, Jonathan J, et autres
Publié: (2026)

A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR
par: Mao, Xinyu, et autres
Publié: (2024)

Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
par: Schlatt, Ferdinand, et autres
Publié: (2024)

Reassessing Large Language Model Boolean Query Generation for Systematic Reviews
par: Wang, Shuai, et autres
Publié: (2025)

AutoBool: An Reinforcement-Learning trained LLM for Effective Automated Boolean Query Generation for Systematic Reviews
par: Wang, Shuai, 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)

Pseudo Relevance Feedback is Enough to Close the Gap Between Small and Large Dense Retrieval Models
par: Li, Hang, et autres
Publié: (2025)

Beyond Chunk-Then-Embed: A Comprehensive Taxonomy and Evaluation of Document Chunking Strategies for Information Retrieval
par: Zhou, Yongjie, et autres
Publié: (2026)

Can It Reach the Generator? Investigating the Survival of Prompt-Injection Attacks in Realistic RAG Settings
par: Yin, Yu, et autres
Publié: (2026)

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

FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
par: Wang, Shuai, 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)

Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
par: Wang, Hangyu, et autres
Publié: (2024)

Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?
par: Li, Minghan, et autres
Publié: (2023)

MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers
par: Guo, Fang, et autres
Publié: (2024)

Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation
par: Zhu, Jiachen, et autres
Publié: (2024)

Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning
par: Zhuang, Jun, et autres
Publié: (2024)

Beyond GeneGPT: A Multi-Agent Architecture with Open-Source LLMs for Enhanced Genomic Question Answering
par: Chen, Haodong, et autres
Publié: (2025)

Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing
par: Yan, Le, et autres
Publié: (2024)

Layer-wise Token Compression for Efficient Document Reranking
par: Zhuang, Shengyao, et autres
Publié: (2026)