mlsanityData to 14 Oct 2026

Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM

arXiv 2312.15450 ↗First posted 24 Dec 2023cs.IRcited by 18

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

Search engines are crucial as they provide an efficient and easy way to access vast amounts of information on the internet for diverse information needs. User queries, even with a specific need, can differ significantly. Prior research has explored the resilience of ranking models against typical query variations like paraphrasing, misspellings, and order changes. Yet, these works overlook how diverse demographics uniquely formulate identical queries. For instance, older individuals tend to construct queries more naturally and in varied order compared to other groups. This demographic diversity necessitates enhancing the adaptability of ranking models to diverse query formulations. To this end, in this paper, we propose a framework that integrates a novel rewriting pipeline that rewrites queries from various demographic perspectives and a novel framework to enhance ranking robustness. To be specific, we use Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness. Our extensive experimentation on both public and industrial datasets assesses the efficacy of our query rewriting approach and the enhanced accuracy and robustness of the ranking model. The findings highlight the sophistication and effectiveness of our proposed model.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 42 of its 85 references that are in the map. First posted on arXiv 24 Dec 2023.

Builds on

42 of 85 references.

References · 42

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  5. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  6. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
  7. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  8. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  9. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 849
  10. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  11. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 463
  12. 2023Character-LLM: A Trainable Agent for Role-Playingcited by 339
30 more
  1. 2020K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapterscited by 319
  2. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 298
  3. 2019ERNIE 2.0: A Continual Pre-training Framework for Language Understandingcited by 231
  4. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 213
  5. 2023Query2doc: Query Expansion with Large Language Modelscited by 149
  6. 2017A Deep Relevance Matching Model for Ad-hoc Retrievalcited by 126
  7. 2019Deeper Text Understanding for IR with Contextual Neural Language Modelingcited by 113
  8. 2023When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applicationscited by 111
  9. 2018Deep Reinforcement Learning for Page-wise Recommendationscited by 100
  10. 2020Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbotscited by 73
  11. 2018Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learningcited by 71
  12. 2023Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewritingcited by 69
  13. 2023Query Expansion by Prompting Large Language Modelscited by 57
  14. 2021Evaluating the Robustness of Retrieval Pipelines with Query Variation Generatorscited by 34
  15. 2023KuaiSim: A Comprehensive Simulator for Recommender Systemscited by 30
  16. 2023Large Language Models are Strong Zero-Shot Retrievercited by 26
  17. 2021B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrievalcited by 25
  18. 2023AutoDenoise: Automatic Data Instance Denoising for Recommendationscited by 23
  19. 2023MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansioncited by 23
  20. 2022CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typoscited by 22
  21. 2022QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillationcited by 21
  22. 2023HAMUR: Hyper Adapter for Multi-Domain Recommendationcited by 21
  23. 2023Exploration and Regularization of the Latent Action Space in Recommendationcited by 17
  24. 2021Are Neural Ranking Models Robust?cited by 16
  25. 2023User Retention-oriented Recommendation with Decision Transformercited by 14
  26. 2023A Study on FGSM Adversarial Training for Neural Retrievalcited by 10
  27. 2023Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrievalcited by 9
  28. 2023Query Understanding in the Age of Large Language Modelscited by 7
  29. 2023Typo-Robust Representation Learning for Dense Retrievalcited by 6
  30. 2023Noise-Robust Dense Retrieval via Contrastive Alignment Post Trainingcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 8; 2026: 3.

18 papers.

Sort:

By area:

  1. 2023Large Language Models for Generative Information Extraction: A Surveycited by 185
  2. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  3. 2024G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Modelscited by 34
  4. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 33
  5. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 33
  6. 2024BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filteringcited by 29
  7. 2024Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generationcited by 13
  8. 2026DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agentcited by 10
  9. 2024ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systemscited by 9
  10. 2024Stepwise Reasoning Error Disruption Attack of LLMscited by 8
  11. 2025TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Frameworkcited by 7
  12. 2025Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entitiescited by 4
6 more
  1. 2025TAPO: Task-Referenced Adaptation for Prompt Optimizationcited by 3
  2. 2025MassTool: A Multi-Task Search-Based Tool Retrieval Framework for Large Language Modelscited by 3
  3. 2025Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systemscited by 2
  4. 2025Model Merging for Knowledge Editingcited by 2
  5. 2026LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrievalcited by 0
  6. 2026Improving Neural Retrieval with Attribution-Guided Query Rewritingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Multi-Agent Perspective on Modern Information Retrieval
  2. 2026Prism-Reranker: Beyond Relevance Scoring -- Jointly Producing Contributions and Evidence for Agentic Retrieval
  3. 2026Agent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval
  4. 2024Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization
  5. 2026Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
  6. 2025REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
  7. 2025DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation
  8. 2026LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems