Copertina

InRanker: Distilled Rankers for Zero-shot Information Retrieval

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Laitz, Thiago, Papakostas, Konstantinos, Lotufo, Roberto, Nogueira, Rodrigo
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Information Retrieval
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ExaRanker-Open: Synthetic Explanation for IR using Open-Source LLMs
di: Ferraretto, Fernando, et al.
Pubblicazione: (2024)

An Investigation of Prompt Variations for Zero-shot LLM-based Rankers
di: Sun, Shuoqi, et al.
Pubblicazione: (2024)

RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
di: Luo, Sichun, et al.
Pubblicazione: (2023)

Evaluating the Explainability of Neural Rankers
di: Pandian, Saran, et al.
Pubblicazione: (2024)

R1-Ranker: Teaching LLM Rankers to Reason
di: Feng, Tao, et al.
Pubblicazione: (2025)

LRanker: LLM Ranker for Massive Candidates
di: Feng, Tao, et al.
Pubblicazione: (2026)

Guiding Retrieval using LLM-based Listwise Rankers
di: Rathee, Mandeep, et al.
Pubblicazione: (2025)

Large Language Models are Zero-Shot Rankers for Recommender Systems
di: Hou, Yupeng, et al.
Pubblicazione: (2023)

LLM as Explainable Re-Ranker for Recommendation System
di: Wang, Yaqi, et al.
Pubblicazione: (2025)

Rankers, Judges, and Assistants: Towards Understanding the Interplay of LLMs in Information Retrieval Evaluation
di: Balog, Krisztian, et al.
Pubblicazione: (2025)

Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
di: Chen, Shijie, et al.
Pubblicazione: (2024)

The Surprising Effectiveness of Rankers Trained on Expanded Queries
di: Anand, Abhijit, et al.
Pubblicazione: (2024)

Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels
di: Zhuang, Honglei, et al.
Pubblicazione: (2023)

Reproducing and Extending Causal Insights Into Term Frequency Computation in Neural Rankers
di: van Marken, Cile, et al.
Pubblicazione: (2025)

When More Reformulations Hurt: Avoiding Drift using Ranker Feedback
di: Venktesh, V, et al.
Pubblicazione: (2026)

OneRanker: Unified Generation and Ranking with One Model in Industrial Advertising Recommendation
di: Sun, Dekai, et al.
Pubblicazione: (2026)

Quati: A Brazilian Portuguese Information Retrieval Dataset from Native Speakers
di: Bueno, Mirelle, et al.
Pubblicazione: (2024)

Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models
di: Zhuang, Shengyao, et al.
Pubblicazione: (2024)

Re-Rankers as Relevance Judges
di: Meng, Chuan, et al.
Pubblicazione: (2026)

Make Large Language Model a Better Ranker
di: Chao, Wen-Shuo, et al.
Pubblicazione: (2024)

Orbit: A Framework for Designing and Evaluating Multi-objective Rankers
di: Yang, Chenyang, et al.
Pubblicazione: (2024)

HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers
di: Santra, Payel, et al.
Pubblicazione: (2025)

Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?
di: Li, Minghan, et al.
Pubblicazione: (2023)

ListConRanker: A Contrastive Text Reranker with Listwise Encoding
di: Liu, Junlong, et al.
Pubblicazione: (2025)

Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs
di: Yu, Puxuan, et al.
Pubblicazione: (2024)

RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation
di: Meng, Zeyuan, et al.
Pubblicazione: (2025)

MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers
di: Guo, Fang, et al.
Pubblicazione: (2024)

ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language
di: Piau, Marcos, et al.
Pubblicazione: (2024)

Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
di: Wang, Yuan, et al.
Pubblicazione: (2024)

DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation
di: Chandradevan, Ramraj, et al.
Pubblicazione: (2024)

InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers
di: Boytsov, Leonid, et al.
Pubblicazione: (2023)

Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
di: Qin, Zhen, et al.
Pubblicazione: (2023)

Domain-Adaptive Dense Retrieval for Brazilian Legal Search
di: Pereira, Jayr, et al.
Pubblicazione: (2026)

Rich-Media Re-Ranker: A User Satisfaction-Driven LLM Re-ranking Framework for Rich-Media Search
di: Guo, Zihao, et al.
Pubblicazione: (2026)

Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization
di: Xing, Tiancheng, et al.
Pubblicazione: (2025)

TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs
di: Cipollone, Daniele, et al.
Pubblicazione: (2025)

MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems
di: Xiao, Qiang Charles, et al.
Pubblicazione: (2024)

Linear-PAL: A Lightweight Ranker for Mitigating Shortcut Learning in Personalized, High-Bias Tabular Ranking
di: Pawar, Vipul Dinesh
Pubblicazione: (2025)

All Eyes on the Ranker: Participatory Auditing to Surface Blind Spots in Ranked Search Results
di: Rezk, Anna Marie, et al.
Pubblicazione: (2026)

RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback
di: Mordo, Tommy, et al.
Pubblicazione: (2025)