When More Reformulations Hurt: Avoiding Drift using Ranker Feedback
Fuente:
arXiv
Saved in:
| Main Authors: | Venktesh, V, Rathee, Mandeep, Anand, Avishek |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA
by: Venktesh, V, et al.
Published: (2025)
by: Venktesh, V, et al.
Published: (2025)
Guiding Retrieval using LLM-based Listwise Rankers
by: Rathee, Mandeep, et al.
Published: (2025)
by: Rathee, Mandeep, et al.
Published: (2025)
Test-time Corpus Feedback: From Retrieval to RAG
by: Rathee, Mandeep, et al.
Published: (2025)
by: Rathee, Mandeep, et al.
Published: (2025)
Reproducing Adaptive Reranking for Reasoning-Intensive IR
by: Rathee, Mandeep, et al.
Published: (2026)
by: Rathee, Mandeep, et al.
Published: (2026)
Breaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets
by: Rathee, Mandeep, et al.
Published: (2025)
by: Rathee, Mandeep, et al.
Published: (2025)
The Surprising Effectiveness of Rankers Trained on Expanded Queries
by: Anand, Abhijit, et al.
Published: (2024)
by: Anand, Abhijit, et al.
Published: (2024)
Quam: Adaptive Retrieval through Query Affinity Modelling
by: Rathee, Mandeep, et al.
Published: (2024)
by: Rathee, Mandeep, et al.
Published: (2024)
DEXTER: A Benchmark for open-domain Complex Question Answering using LLMs
by: Prabhu, Venktesh V. Deepali, et al.
Published: (2024)
by: Prabhu, Venktesh V. Deepali, et al.
Published: (2024)
A Benchmark for Open-Domain Numerical Fact-Checking Enhanced by Claim Decomposition
by: Venktesh, V, et al.
Published: (2025)
by: Venktesh, V, et al.
Published: (2025)
Understanding the User: An Intent-Based Ranking Dataset
by: Anand, Abhijit, et al.
Published: (2024)
by: Anand, Abhijit, et al.
Published: (2024)
Trust but Verify! A Survey on Verification Design for Test-time Scaling
by: Venktesh, V, et al.
Published: (2025)
by: Venktesh, V, et al.
Published: (2025)
FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking
by: Nanekhan, Kevin, et al.
Published: (2025)
by: Nanekhan, Kevin, et al.
Published: (2025)
ir_explain: a Python Library of Explainable IR Methods
by: Saha, Sourav, et al.
Published: (2024)
by: Saha, Sourav, et al.
Published: (2024)
On Listwise Reranking for Corpus Feedback
by: Yoon, Soyoung, et al.
Published: (2025)
by: Yoon, Soyoung, et al.
Published: (2025)
FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for Fact-Checking
by: V, Venktesh, et al.
Published: (2025)
by: V, Venktesh, et al.
Published: (2025)
InRanker: Distilled Rankers for Zero-shot Information Retrieval
by: Laitz, Thiago, et al.
Published: (2024)
by: Laitz, Thiago, et al.
Published: (2024)
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
by: Luo, Sichun, et al.
Published: (2023)
by: Luo, Sichun, et al.
Published: (2023)
Evaluating the Explainability of Neural Rankers
by: Pandian, Saran, et al.
Published: (2024)
by: Pandian, Saran, et al.
Published: (2024)
LRanker: LLM Ranker for Massive Candidates
by: Feng, Tao, et al.
Published: (2026)
by: Feng, Tao, et al.
Published: (2026)
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
by: Heuss, Maria, et al.
Published: (2024)
by: Heuss, Maria, et al.
Published: (2024)
LLM as Explainable Re-Ranker for Recommendation System
by: Wang, Yaqi, et al.
Published: (2025)
by: Wang, Yaqi, et al.
Published: (2025)
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)
R1-Ranker: Teaching LLM Rankers to Reason
by: Feng, Tao, et al.
Published: (2025)
by: Feng, Tao, et al.
Published: (2025)
Reproducing and Extending Causal Insights Into Term Frequency Computation in Neural Rankers
by: van Marken, Cile, et al.
Published: (2025)
by: van Marken, Cile, et al.
Published: (2025)
OneRanker: Unified Generation and Ranking with One Model in Industrial Advertising Recommendation
by: Sun, Dekai, et al.
Published: (2026)
by: Sun, Dekai, et al.
Published: (2026)
RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation
by: Meng, Zeyuan, et al.
Published: (2025)
by: Meng, Zeyuan, et al.
Published: (2025)
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs
by: Yu, Puxuan, et al.
Published: (2024)
by: Yu, Puxuan, 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)
Large Language Models are Zero-Shot Rankers for Recommender Systems
by: Hou, Yupeng, et al.
Published: (2023)
by: Hou, Yupeng, et al.
Published: (2023)
An Investigation of Prompt Variations for Zero-shot LLM-based Rankers
by: Sun, Shuoqi, et al.
Published: (2024)
by: Sun, Shuoqi, et al.
Published: (2024)
RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback
by: Mordo, Tommy, et al.
Published: (2025)
by: Mordo, Tommy, et al.
Published: (2025)
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance
by: Jang, Yunah, et al.
Published: (2023)
by: Jang, Yunah, et al.
Published: (2023)
Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation
by: Zhang, Luankang, et al.
Published: (2026)
by: Zhang, Luankang, et al.
Published: (2026)
When "Better" Prompts Hurt: Evaluation-Driven Iteration for LLM Applications
by: Commey, Daniel
Published: (2026)
by: Commey, Daniel
Published: (2026)
HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers
by: Santra, Payel, et al.
Published: (2025)
by: Santra, Payel, et al.
Published: (2025)
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?
by: Li, Minghan, et al.
Published: (2023)
by: Li, Minghan, et al.
Published: (2023)
ListConRanker: A Contrastive Text Reranker with Listwise Encoding
by: Liu, Junlong, et al.
Published: (2025)
by: Liu, Junlong, et al.
Published: (2025)
It's High Time: A Survey of Temporal Question Answering
by: Piryani, Bhawna, et al.
Published: (2025)
by: Piryani, Bhawna, et al.
Published: (2025)
TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions
by: Abdallah, Abdelrahman, et al.
Published: (2025)
by: Abdallah, Abdelrahman, et al.
Published: (2025)
Rich-Media Re-Ranker: A User Satisfaction-Driven LLM Re-ranking Framework for Rich-Media Search
by: Guo, Zihao, et al.
Published: (2026)
by: Guo, Zihao, et al.
Published: (2026)
Similar Items
-
SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA
by: Venktesh, V, et al.
Published: (2025) -
Guiding Retrieval using LLM-based Listwise Rankers
by: Rathee, Mandeep, et al.
Published: (2025) -
Test-time Corpus Feedback: From Retrieval to RAG
by: Rathee, Mandeep, et al.
Published: (2025) -
Reproducing Adaptive Reranking for Reasoning-Intensive IR
by: Rathee, Mandeep, et al.
Published: (2026) -
Breaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets
by: Rathee, Mandeep, et al.
Published: (2025)