---
title: "Who cites FanOutQA: A Multi-Hop, — 36 citing papers"
description: "36 papers of the Elamur Research library cite «FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [FanOutQA: A Multi-Hop, Multi-Document Question…](https://md.research.elamur.ai/free/en/papers/zhu-2024-fanoutqa-multi-hop-multi-document/) / cited by

# Who cites «FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models»

36 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models»](https://md.research.elamur.ai/free/en/papers/zhu-2024-fanoutqa-multi-hop-multi-document/)

1. **[DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking](https://md.research.elamur.ai/free/en/papers/li-2025-deepsolution-boosting-complex-engineering-solution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhuoqun Li, Haiyang Yu, Xuanang Chen, et al.*2025 · 10 citations*
2. **[MoNaCo: More Natural and Complex Questions for Reasoning Across Dozens of Documents](https://md.research.elamur.ai/free/en/papers/wolfson-2026-monaco-more-natural-complex-questions/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tomer Wolfson, Harsh Trivedi, Mor Geva, et al.*2026 · 5 citations*
3. **[CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability](https://md.research.elamur.ai/free/en/papers/peng-2025-cafe-retrieval-head-based-coarse/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Han Peng, Jinhao Jiang, Zican Dong, et al.*2025 · 4 citations*
4. **[KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation](https://md.research.elamur.ai/free/en/papers/tatarinov-2026-kg-mulqa-framework-kg-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nikita Tatarinov, Vidhyakshaya Kannan, Haricharana Srinivasa, et al.*2026 · 1 citation*
5. **[An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA](https://md.research.elamur.ai/free/en/papers/sharma-2026-answer-is-just-start-related/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Saransh Sharma, Pritika Ramu, Aparna Garimella, et al.*2026*
6. **[Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA](https://md.research.elamur.ai/free/en/papers/maheshwary-2026-augmenting-llm-reasoning-dynamic-notes/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Rishabh Maheshwary, Masoud Hashemi, Khyati Mahajan, et al.*2026*
7. **[Data Foundations of Long-Context Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/sun-2026-data-foundations-long-context-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zechen Sun, Yuyang Sun, Zhaochen Su, et al.*2026*
8. **[MARCH: Evaluating the Intersection of Ambiguity Interpretation and Multi-hop Inference](https://md.research.elamur.ai/free/en/papers/park-2026-march-evaluating-intersection-ambiguity-interpretation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jeonghyun Park, Ingeol Baek, Seunghyun Yoon, et al.*2026*

28 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/zhu-2024-fanoutqa-multi-hop-multi-document/) — FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models