Harnessing Pairwise Ranking Prompting Through Sample-Efficient Ranking Distillation
Fuente:
arXiv
Saved in:
| Main Authors: | Wu, Junru, Yan, Le, Qin, Zhen, Zhuang, Honglei, C., Paul Suganthan G., Liu, Tianqi, Dong, Zhe, Wang, Xuanhui, Oosterhuis, Harrie |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing
by: Yan, Le, et al.
Published: (2024)
by: Yan, Le, et al.
Published: (2024)
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
by: Qin, Zhen, et al.
Published: (2023)
by: Qin, Zhen, et al.
Published: (2023)
Learning to Rank with Variable Result Presentation Lengths
by: Knyazev, Norman, et al.
Published: (2025)
by: Knyazev, Norman, et al.
Published: (2025)
Optimizing Compound Retrieval Systems
by: Oosterhuis, Harrie, et al.
Published: (2025)
by: Oosterhuis, Harrie, et al.
Published: (2025)
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank
by: Gupta, Shashank, et al.
Published: (2024)
by: Gupta, Shashank, et al.
Published: (2024)
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees
by: Kang, Jingwei, et al.
Published: (2024)
by: Kang, Jingwei, et al.
Published: (2024)
Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I
by: Oosterhuis, Harrie, et al.
Published: (2024)
by: Oosterhuis, Harrie, et al.
Published: (2024)
Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels
by: Zhuang, Honglei, et al.
Published: (2023)
by: Zhuang, Honglei, et al.
Published: (2023)
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
by: Gupta, Shashank, et al.
Published: (2024)
by: Gupta, Shashank, et al.
Published: (2024)
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?
by: Lyu, Lijun, et al.
Published: (2024)
by: Lyu, Lijun, et al.
Published: (2024)
A Non-Parametric Choice Model That Learns How Users Choose Between Recommended Options
by: Krause, Thorsten, et al.
Published: (2025)
by: Krause, Thorsten, et al.
Published: (2025)
A First Look at Selection Bias in Preference Elicitation for Recommendation
by: Gupta, Shashank, et al.
Published: (2024)
by: Gupta, Shashank, et al.
Published: (2024)
A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
by: Zhuang, Shengyao, et al.
Published: (2023)
by: Zhuang, Shengyao, et al.
Published: (2023)
Following the Eye-Tracking Evidence: Established Web-Search Assumptions Fail in Carousel Interfaces
by: Kang, Jingwei, et al.
Published: (2026)
by: Kang, Jingwei, et al.
Published: (2026)
Beyond Pairwise Learning-To-Rank At Airbnb
by: Haldar, Malay, et al.
Published: (2025)
by: Haldar, Malay, et al.
Published: (2025)
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation
by: Kannen, Nithish, et al.
Published: (2024)
by: Kannen, Nithish, et al.
Published: (2024)
From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking
by: Zhao, Yuhan, et al.
Published: (2024)
by: Zhao, Yuhan, et al.
Published: (2024)
Optimal Baseline Corrections for Off-Policy Contextual Bandits
by: Gupta, Shashank, et al.
Published: (2024)
by: Gupta, Shashank, et al.
Published: (2024)
Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models
by: Kang, Jingwei, et al.
Published: (2025)
by: Kang, Jingwei, et al.
Published: (2025)
Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model
by: Sinhababu, Nilanjan, et al.
Published: (2024)
by: Sinhababu, Nilanjan, et al.
Published: (2024)
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?
by: Li, Minghan, et al.
Published: (2023)
by: Li, Minghan, et al.
Published: (2023)
Bounded-Abstention Pairwise Learning to Rank
by: Ferrara, Antonio, et al.
Published: (2025)
by: Ferrara, Antonio, et al.
Published: (2025)
Pairwise Ranking Loss for Multi-Task Learning in Recommender Systems
by: Durmus, Furkan, et al.
Published: (2024)
by: Durmus, Furkan, et al.
Published: (2024)
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
by: Huang, Jin, et al.
Published: (2024)
by: Huang, Jin, et al.
Published: (2024)
Adaptive Orchestration of Modular Generative Information Access Systems
by: Hoveyda, Mohanna, et al.
Published: (2025)
by: Hoveyda, Mohanna, et al.
Published: (2025)
Disentangling Locality and Entropy in Ranking Distillation
by: Parry, Andrew, et al.
Published: (2025)
by: Parry, Andrew, et al.
Published: (2025)
RankMixer: Scaling Up Ranking Models in Industrial Recommenders
by: Zhu, Jie, et al.
Published: (2025)
by: Zhu, Jie, et al.
Published: (2025)
Distilled Neural Networks for Efficient Learning to Rank
by: Nardini, F. M., et al.
Published: (2022)
by: Nardini, F. M., et al.
Published: (2022)
Training on the Test Model: Contamination in Ranking Distillation
by: Kalal, Vishakha Suresh, et al.
Published: (2024)
by: Kalal, Vishakha Suresh, et al.
Published: (2024)
Multi-objective Learning to Rank by Model Distillation
by: Tang, Jie, et al.
Published: (2024)
by: Tang, Jie, et al.
Published: (2024)
Retrieval Augmented Conversational Recommendation with Reinforcement Learning
by: Yue, Zhenrui, et al.
Published: (2026)
by: Yue, Zhenrui, et al.
Published: (2026)
Leveraging LLMs for Unsupervised Dense Retriever Ranking
by: Khramtsova, Ekaterina, et al.
Published: (2024)
by: Khramtsova, Ekaterina, et al.
Published: (2024)
RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation
by: Fu, Kairui, et al.
Published: (2026)
by: Fu, Kairui, et al.
Published: (2026)
NEAR$^2$: A Nested Embedding Approach to Efficient Product Retrieval and Ranking
by: Qian, Shenbin, et al.
Published: (2025)
by: Qian, Shenbin, et al.
Published: (2025)
Centrality-aware Product Retrieval and Ranking
by: Saadany, Hadeel, et al.
Published: (2024)
by: Saadany, Hadeel, et al.
Published: (2024)
RankTower: A Synergistic Framework for Enhancing Two-Tower Pre-Ranking Model
by: Yan, YaChen, et al.
Published: (2024)
by: Yan, YaChen, et al.
Published: (2024)
PairDistill: Pairwise Relevance Distillation for Dense Retrieval
by: Huang, Chao-Wei, et al.
Published: (2024)
by: Huang, Chao-Wei, et al.
Published: (2024)
Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking
by: Schlatt, Ferdinand, et al.
Published: (2024)
by: Schlatt, Ferdinand, et al.
Published: (2024)
MERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking
by: Quan, Shigang, et al.
Published: (2025)
by: Quan, Shigang, et al.
Published: (2025)
Towards Efficient Pareto-optimal Utility-Fairness between Groups in Repeated Rankings
by: Mai, Phuong Dinh, et al.
Published: (2024)
by: Mai, Phuong Dinh, et al.
Published: (2024)
Similar Items
-
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing
by: Yan, Le, et al.
Published: (2024) -
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
by: Qin, Zhen, et al.
Published: (2023) -
Learning to Rank with Variable Result Presentation Lengths
by: Knyazev, Norman, et al.
Published: (2025) -
Optimizing Compound Retrieval Systems
by: Oosterhuis, Harrie, et al.
Published: (2025) -
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank
by: Gupta, Shashank, et al.
Published: (2024)