mlsanityData to 7 Oct 2026

Task-Oriented Query Reformulation with Reinforcement Learning

arXiv 1704.04572 ↗First posted 15 Apr 2017cs.IRcited by 39

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Abstract (arXiv)

Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2017.

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Citing papers per year: 2020: 1; 2021: 9; 2022: 7; 2023: 8; 2024: 6; 2025: 2; 2026: 6.

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  1. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  2. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
  3. 2023Augmented Language Models: a Surveycited by 398
  4. 2021Internet-Augmented Dialogue Generationcited by 217
  5. 2022Evaluating Human-Language Model Interactioncited by 90
  6. 2021BERTese: Learning to Speak to BERTcited by 82
  7. 2024Bridging the Preference Gap between Retrievers and LLMscited by 69
  8. 2021CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learningcited by 68
  9. 2024RaFe: Ranking Feedback Improves Query Rewriting for RAGcited by 47
  10. 2024A Survey of Conversational Searchcited by 22
  11. 2021Boosting Search Engines with Interactive Agentscited by 19
  12. 2022Reinforced Question Rewriting for Conversational Question Answeringcited by 19
27 more
  1. 2021Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphscited by 18
  2. 2024Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrievalcited by 17
  3. 2023Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answeringcited by 15
  4. 2020Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Taskscited by 12
  5. 2022Asking for Knowledge: Training RL Agents to Query External Knowledge Using Languagecited by 10
  6. 2022Can Question Rewriting Help Conversational Question Answering?cited by 8
  7. 2023Query Understanding in the Age of Large Language Modelscited by 7
  8. 2023Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generationcited by 7
  9. 2022Decoding a Neural Retriever's Latent Space for Query Suggestioncited by 5
  10. 2023PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classificationcited by 4
  11. 2023Conversational Question Answering with Reformulations over Knowledge Graphcited by 4
  12. 2021Weakly Supervised Pre-Training for Multi-Hop Retrievercited by 3
  13. 2021High Quality Related Search Query Suggestions using Deep Reinforcement Learningcited by 3
  14. 2023BM25 Query Augmentation Learned End-to-Endcited by 3
  15. 2022Query Rewriting for Effective Misinformation Discoverycited by 2
  16. 2023Interactive Editing for Text Summarizationcited by 2
  17. 2024An Extremely Data-efficient and Generative LLM-based Reinforcement Learning Agent for Recommenderscited by 2
  18. 2021Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independencecited by 1
  19. 2024GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrievalcited by 1
  20. 2025QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewritingcited by 1
  21. 2025QueStER: Query Specification for Generative keyword-based Retrievalcited by 1
  22. 2026Understanding the Behaviors of Environment-aware Information Retrievalcited by 1
  23. 2026Improving Neural Retrieval with Attribution-Guided Query Rewritingcited by 0
  24. 2026No One Size Fits All: QueryBandits for Hallucination Mitigationcited by 0
  25. 2026Offline RL for Adaptive Policy Retrieval in Prior Authorizationcited by 0
  26. 2026Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Searchcited by 0
  27. 2026Harness-G: A Graph-Structured Harness for Search Agentscited by 0