--- title: "Who cites The Web as a Knowledge-Base for Answering…" description: "316 papers of the Elamur Research library cite «The Web as a Knowledge-Base for Answering Complex Questions», found in their bibliographies; the most…" canonical: "https://research.elamur.ai/free/en/papers/talmor-2018-web-as-knowledge-base-answering/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/talmor-2018-web-as-knowledge-base-answering/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [The Web as a Knowledge-Base for Answering Complex…](https://md.research.elamur.ai/free/en/papers/talmor-2018-web-as-knowledge-base-answering/) / cited by # Who cites «The Web as a Knowledge-Base for Answering Complex Questions» 316 papers of our library cite it (of the 140,884 whose bibliographies we read) · 22 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/talmor-2018-web-as-knowledge-base-answering/) 1. [**HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering**Zhilin Yang, Peng Qi, Saizheng Zhang, et al.*2018 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/) 2. [**DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs**Dheeru Dua, Yizhong Wang, Pradeep Dasigi, et al.*2019 · 941 citations*](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/) 3. [**Towards Reasoning in Large Language Models: A Survey**Jie Huang, Kevin Chen-Chuan Chang*2023 · 775 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/) 4. [**Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps**Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, et al.*2020 · 735 citations*](https://md.research.elamur.ai/free/en/papers/ho-2020-constructing-multi-hop-qa-dataset/) 5. [**KILT: a Benchmark for Knowledge Intensive Language Tasks**Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.*2020 · 663 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/) 6. [**FinQA: A Dataset of Numerical Reasoning over Financial Data**Zhiyu Chen, Wenhu Chen, Charese Smiley, et al.*2021 · 510 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/) 7. [**UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models**Tianbao Xie, Chen Henry Wu, Peng Shi, et al.*2022 · 405 citations*](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/) 8. [**AgentBench: Evaluating LLMs as Agents**Xiao Liu, Hao Yu, Hanchen Zhang, et al.*2023 · 404 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-agentbench-evaluating-llms-agents/) 9. [**TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance**Fengbin Zhu, Wenqiang Lei, Youcheng Huang, et al.*2021 · 396 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2021-tat-qa-question-answering-benchmark/) 10. [**HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data**Wenhu Chen, Hanwen Zha, Zhiyu Chen, et al.*2020 · 376 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-hybridqa-dataset-multi-hop-question/) 11. [**Augmented Language Models: a Survey**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 362 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/) 12. [**UL2: Unifying Language Learning Paradigms**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 334 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/) 13. [**Multi-hop Reading Comprehension through Question Decomposition and Rescoring**Sewon Min, Victor Zhong, Luke Zettlemoyer, et al.*2019 · 306 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-multi-hop-reading-comprehension-through/) 14. [**Can You Unpack That? Learning to Rewrite Questions-in-Context**Ahmed Elgohary, Denis Peskov, Jordan Boyd-Graber*2019 · 244 citations*](https://md.research.elamur.ai/free/en/papers/elgohary-2019-can-you-unpack-that-learning/) 15. [**Compositional Questions Do Not Necessitate Multi-hop Reasoning**Sewon Min, Eric Wallace, Sameer Singh, et al.*2019 · 231 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-compositional-questions-do-not-necessitate/) 16. [**Unsupervised Question Decomposition for Question Answering**Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/) 17. [**QuALITY: Question Answering with Long Input Texts, Yes!**Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.*2022 · 161 citations*](https://md.research.elamur.ai/free/en/papers/pang-2022-quality-question-answering-long-input/) 18. [**ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings**Shibo Hao, Tianyang Liu, Zhen Wang, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/hao-2023-toolkengpt-augmenting-frozen-language-models/) 19. [**StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts**Zhengxiang Shi, Qiang Zhang, Aldo Lipani*2022 · 70 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-stepgame-new-benchmark-robust-multi/) 20. [**Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering**Runxuan Liu, Bei Luo, Jiaqi Li, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-ontology-guided-reverse-thinking-makes/) 21. [**PolyglotQL: A Pipeline for Multilingual Text-to-SPARQL Dataset Generation**Julio Perez, Fabio Barth, Georg Rehm*2026*](https://md.research.elamur.ai/free/en/papers/perez-2026-polyglotql-pipeline-multilingual-text-sparql/) 22. [**Recent Advances in Text-attributed Graph Learning via Large Models (大模型驱动的文本属性图学习研究进展)**Yanping Zheng (郑艳萍), Jiarui Ji (季嘉蕊), Yuwei Hu (胡雨韦), et al.*2026*](https://md.research.elamur.ai/free/en/papers/zheng-2026-recent-advances-text/) 294 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/talmor-2018-web-as-knowledge-base-answering/) — The Web as a Knowledge-Base for Answering Complex Questions