---
title: "Who cites Compositional Semantic Parsing on…"
description: "404 papers of the Elamur Research library cite «Compositional Semantic Parsing on Semi-Structured Tables», found in their bibliographies; the most cited…"
canonical: "https://research.elamur.ai/free/en/papers/pasupat-2015-compositional-semantic-parsing-semi-structured/cited-by/"
---
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# Who cites «Compositional Semantic Parsing on Semi-Structured Tables»

404 papers of our library cite it (of the 140,884 whose bibliographies we read) · 38 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/pasupat-2015-compositional-semantic-parsing-semi-structured/)

1. [**TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension**Mandar Joshi, Eunsol Choi, Daniel Weld, et al.*2017 · 3,204 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/)
2. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
3. [**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/)
4. [**TaPas: Weakly Supervised Table Parsing via Pre-training**Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, et al.*2020 · 844 citations*](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/)
5. [**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/)
6. [**ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning**Ahmed Masry, Do Xuan Long, Jia Qing Tan, et al.*2022 · 744 citations*](https://md.research.elamur.ai/free/en/papers/masry-2022-chartqa-benchmark-question-answering-about/)
7. [**TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data**Pengcheng Yin, Graham Neubig, Wen-tau Yih, et al.*2020 · 690 citations*](https://md.research.elamur.ai/free/en/papers/yin-2020-tabert-pretraining-joint-understanding-textual/)
8. [**Learning to Compose Neural Networks for Question Answering**Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al.*2016 · 547 citations*](https://md.research.elamur.ai/free/en/papers/andreas-2016-learning-compose-neural-networks-question/)
9. [**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/)
10. [**TabFact: A Large-scale Dataset for Table-based Fact Verification**Wenhu Chen, Hongmin Wang, Jianshu Chen, et al.*2019 · 508 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
11. [**ToTTo: A Controlled Table-To-Text Generation Dataset**Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al.*2020 · 479 citations*](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/)
12. [**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/)
13. [**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/)
14. [**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/)
15. [**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/)
16. [**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/)
17. [**The Web as a Knowledge-Base for Answering Complex Questions**Alon Talmor, Jonathan Berant*2018 · 316 citations*](https://md.research.elamur.ai/free/en/papers/talmor-2018-web-as-knowledge-base-answering/)
18. [**MiniCPM-V: A GPT-4V Level MLLM on Your Phone**Yuan Yao, Tianyu Yu, Ao Zhang, et al.*2024 · 267 citations*](https://md.research.elamur.ai/free/en/papers/yao-2024-minicpm-v-gpt-4v/)
19. [**TAPEX: Table Pre-training via Learning a Neural SQL Executor**Qian Liu, Bei Chen, Jiaqi Guo, et al.*2021 · 254 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-tapex-table-pre-training-learning/)
20. [**From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood**Kelvin Guu, Panupong Pasupat, Evan Liu, et al.*2017 · 230 citations*](https://md.research.elamur.ai/free/en/papers/guu-2017-language-programs-bridging-reinforcement-learning/)
21. [**SParC: Cross-Domain Semantic Parsing in Context**Tao Yu, Rui Zhang, Michihiro Yasunaga, et al.*2019 · 227 citations*](https://md.research.elamur.ai/free/en/papers/yu-2019-sparc-cross-domain-semantic-parsing/)
22. [**Break It Down: A Question Understanding Benchmark**Tomer Wolfson, Mor Geva, Ankit Gupta, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/wolfson-2020-break-it-down-question-understanding/)
23. [**Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**Pan Lu, Liang Qiu, Kai-Wei Chang, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-dynamic-prompt-learning-policy-gradient/)
24. [**Logical Natural Language Generation from Open-Domain Tables**Wenhu Chen, Jianshu Chen, Yu Su, et al.*2020 · 209 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-logical-natural-language-generation-open/)
25. [**What matters when building vision-language models?**Hugo Laurençon, Leo Tronchon, Matthieu Cord, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2024-matters-building-vision-language-models/)
26. [**Large Language Models are few(1)-shot Table Reasoners**Wenhu Chen*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-large-language-models-are-few/)
27. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
28. [**Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs**Shengbang Tong, Ellis Brown, Penghao Wu, et al.*2024 · 173 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-cambrian-1-fully-open-vision/)
29. [**Structure-Grounded Pretraining for Text-to-SQL**Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, et al.*2021 · 166 citations*](https://md.research.elamur.ai/free/en/papers/deng-2021-structure-grounded-pretraining-text-sql/)
30. [**Binding Language Models in Symbolic Languages**Zhoujun Cheng, Tianbao Xie, Peng Shi, et al.*2022 · 165 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2022-binding-language-models-symbolic-languages/)
31. [**DART: Open-Domain Structured Data Record to Text Generation**Linyong Nan, Dragomir Radev, Rui Zhang, et al.*2020 · 154 citations*](https://md.research.elamur.ai/free/en/papers/nan-2020-dart-open-domain-structured-data/)
32. [**Executable Code Actions Elicit Better LLM Agents**Xingyao Wang, Yangyi Chen, Lifan Yuan, et al.*2024 · 143 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-executable-code-actions-elicit-better/)
33. [**LEVER: Learning to Verify Language-to-Code Generation with Execution**Ansong Ni, Srini Iyer, Dragomir Radev, et al.*2023 · 122 citations*](https://md.research.elamur.ai/free/en/papers/ni-2023-lever-learning-verify-language-code/)
34. [**Nemotron-4 340B Technical Report**Nvidia: Bo Adler, Niket Agarwal, Ashwath Aithal, et al.*2024 · 102 citations*](https://md.research.elamur.ai/free/en/papers/adler-2024-nemotron-4-340b-technical-report/)
35. [**WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference**Peter Jansen, Elizabeth Wainwright, Steven Marmorstein, et al.*2018 · 88 citations*](https://md.research.elamur.ai/free/en/papers/jansen-2018-worldtree-corpus-explanation-graphs-elementary/)
36. [**AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data**Rana Alshaikh, Israa Alghanmi, Shelan Jeawak*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/alshaikh-2025-aratable-benchmarking-llms-reasoning-understanding/)
37. [**Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages**Nick McKenna, Xinnuo Xu, Jack Williams, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/mckenna-2026-synthetic-function-demonstrations-improve-generation/)
38. [**A Teacher-Student Approach to Creating Verified Synthetic Clarification and Correction Dialogues for TableQA Tasks**Christian Poelitz, Nick McKenna*2026*](https://md.research.elamur.ai/free/en/papers/poelitz-2026-teacher-student-approach-creating-verified/)

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

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