mlsanityData to 14 Oct 2026

Answering Complex Open-domain Questions Through Iterative Query Generation

arXiv 1910.07000 ↗First posted 15 Oct 2019cs.CLcited by 60

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Abstract (arXiv)

It is challenging for current one-step retrieve-and-read question answering (QA) systems to answer questions like "Which novel by the author of 'Armada' will be adapted as a feature film by Steven Spielberg?" because the question seldom contains retrievable clues about the missing entity (here, the author). Answering such a question requires multi-hop reasoning where one must gather information about the missing entity (or facts) to proceed with further reasoning. We present GoldEn (Gold Entity) Retriever, which iterates between reading context and retrieving more supporting documents to answer open-domain multi-hop questions. Instead of using opaque and computationally expensive neural retrieval models, GoldEn Retriever generates natural language search queries given the question and available context, and leverages off-the-shelf information retrieval systems to query for missing entities. This allows GoldEn Retriever to scale up efficiently for open-domain multi-hop reasoning while maintaining interpretability. We evaluate GoldEn Retriever on the recently proposed open-domain multi-hop QA dataset, HotpotQA, and demonstrate that it outperforms the best previously published model despite not using pretrained language models such as BERT.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Oct 2019.

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Citing papers per year: 2021: 15; 2022: 17; 2023: 12; 2024: 9; 2025: 3; 2026: 4.

60 papers.

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  1. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  2. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  3. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
  4. 2021Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answeringcited by 178
  5. 2023Complex Claim Verification with Evidence Retrieved in the Wildcited by 110
  6. 2023LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugincited by 82
  7. 2022Iteratively Prompt Pre-trained Language Models for Chain of Thoughtcited by 75
  8. 2024StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurizationcited by 71
  9. 2022Generating Literal and Implied Subquestions to Fact-check Complex Claimscited by 66
  10. 2021Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrievalcited by 63
  11. 2021Conversational Question Answering: A Surveycited by 31
  12. 2021Adaptive Information Seeking for Open-Domain Question Answeringcited by 29
38 more
  1. 2021Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrievalcited by 25
  2. 2021What's in a Name? Answer Equivalence For Open-Domain Question Answeringcited by 21
  3. 2022Modeling Multi-hop Question Answering as Single Sequence Predictioncited by 21
  4. 2021Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answeringcited by 19
  5. 2023Chain-of-Skills: A Configurable Model for Open-domain Question Answeringcited by 19
  6. 2024Chain of Logic: Rule-Based Reasoning with Large Language Modelscited by 18
  7. 2021The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architecturescited by 16
  8. 2022Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledgecited by 14
  9. 2023Answering Ambiguous Questions via Iterative Promptingcited by 13
  10. 2021Hopper: Multi-hop Transformer for Spatiotemporal Reasoningcited by 12
  11. 2022From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answeringcited by 12
  12. 2026SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activationcited by 12
  13. 2024Retrieval-Enhanced Machine Learning: Synthesis and Opportunitiescited by 11
  14. 2022Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?cited by 10
  15. 2026DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agentcited by 10
  16. 2023Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answeringcited by 9
  17. 2025PRISM: Agentic Retrieval with LLMs for Multi-Hop Question Answeringcited by 8
  18. 2022Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answeringcited by 7
  19. 2022Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoningcited by 7
  20. 2021Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?cited by 6
  21. 2022Zero-Shot Retrieval with Search Agents and Hybrid Environmentscited by 6
  22. 2024FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answeringcited by 6
  23. 2021Coarse-grained decomposition and fine-grained interaction for multi-hop question answeringcited by 5
  24. 2023HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale Supervisioncited by 5
  25. 2023Single Sequence Prediction over Reasoning Graphs for Multi-hop QAcited by 5
  26. 2025Query Decomposition for RAG: Balancing Exploration-Exploitationcited by 5
  27. 2021Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answeringcited by 4
  28. 2022Double Retrieval and Ranking for Accurate Question Answeringcited by 4
  29. 2022ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chainscited by 4
  30. 2023Performance Prediction for Multi-hop Questionscited by 4
  31. 2024Tree of Reviews: A Tree-based Dynamic Iterative Retrieval Framework for Multi-hop Question Answeringcited by 4
  32. 2024ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generationcited by 4
  33. 2024Multi-Field Adaptive Retrievalcited by 4
  34. 2021Weakly Supervised Pre-Training for Multi-Hop Retrievercited by 3
  35. 2021Extract, Integrate, Compete: Towards Verification Style Reading Comprehensioncited by 3
  36. 2022Prompt-based Conservation Learning for Multi-hop Question Answeringcited by 3
  37. 2023Divide & Conquer for Entailment-aware Multi-hop Evidence Retrievalcited by 3
  38. 2023Response Enhanced Semi-supervised Dialogue Query Generationcited by 2

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