---
title: "Who cites Semantic Parsing on Freebase from…"
description: "785 papers of the Elamur Research library cite «Semantic Parsing on Freebase from Question-Answer Pairs», found in their bibliographies; the most cited…"
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# Who cites «Semantic Parsing on Freebase from Question-Answer Pairs»

785 papers of our library cite it (of the 140,884 whose bibliographies we read) · 74 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/berant-2013-semantic-parsing-freebase-question-answer/)

1. [**Language Models are Few-Shot Learners**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 10,782 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. [**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/)
3. [**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/)
4. [**PaLM: Scaling Language Modeling with Pathways**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 2,603 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
5. [**Toolformer: Language Models Can Teach Themselves to Use Tools**Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al.*2023 · 2,020 citations*](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/)
6. [**Retrieval-Augmented Generation for Large Language Models: A Survey**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,647 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
7. [**Multitask Prompted Training Enables Zero-Shot Task Generalization**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,577 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
8. [**What Makes Good In-Context Examples for GPT-3?**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,235 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
9. [**Latent Retrieval for Weakly Supervised Open Domain Question Answering**Kenton Lee, Ming-Wei Chang, Kristina Toutanova*2019 · 1,234 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-latent-retrieval-weakly-supervised-open/)
10. [**PaLM 2 Technical Report**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 971 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
11. [**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/)
12. [**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/)
13. [**Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/)
14. [**GLM-130B: An Open Bilingual Pre-trained Model**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 775 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
15. [**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/)
16. [**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/)
17. [**Semantic Parsing via Paraphrasing**Jonathan Berant, Percy Liang*2014 · 681 citations*](https://md.research.elamur.ai/free/en/papers/berant-2014-semantic-parsing-paraphrasing/)
18. [**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/)
19. [**A simple neural network module for relational reasoning**Adam Santoro, David Raposo, David G. T. Barrett, et al.*2017 · 590 citations*](https://md.research.elamur.ai/free/en/papers/santoro-2017-simple-neural-network-module-relational/)
20. [**Simple and Effective Multi-Paragraph Reading Comprehension**Christopher Clark, Matt Gardner*2017 · 556 citations*](https://md.research.elamur.ai/free/en/papers/clark-2017-simple-effective-multi-paragraph-reading/)
21. [**Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks**Jason Weston, Antoine Bordes, Sumit Chopra, et al.*2015 · 529 citations*](https://md.research.elamur.ai/free/en/papers/weston-2015-towards-ai-complete-question-answering/)
22. [**Crowdsourcing Multiple Choice Science Questions**Johannes Welbl, Nelson F. Liu, Matt Gardner*2017 · 513 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2017-crowdsourcing-multiple-choice-science-questions/)
23. [**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/)
24. [**A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge**Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al.*2022 · 490 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/)
25. [**ParlAI: A Dialog Research Software Platform**Alexander Miller, Will Feng, Dhruv Batra, et al.*2017 · 483 citations*](https://md.research.elamur.ai/free/en/papers/miller-2017-parlai-dialog-research-software-platform/)
26. [**Semantic Parsing for Single-Relation Question Answering**Wen-tau Yih, Xiaodong He, Christopher Meek*2014 · 483 citations*](https://md.research.elamur.ai/free/en/papers/yih-2014-semantic-parsing-single-relation-question/)
27. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 474 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
28. [**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/)
29. [**Compositional Semantic Parsing on Semi-Structured Tables**Panupong Pasupat, Percy Liang*2015 · 404 citations*](https://md.research.elamur.ai/free/en/papers/pasupat-2015-compositional-semantic-parsing-semi-structured/)
30. [**Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering**Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, et al.*2023 · 403 citations*](https://md.research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/)
31. [**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/)
32. [**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/)
33. [**Question Generation for Question Answering**Nan Duan, Duyu Tang, Peng Chen, et al.*2017 · 370 citations*](https://md.research.elamur.ai/free/en/papers/duan-2017-question-generation-question-answering/)
34. [**Question Answering on Freebase via Relation Extraction and Textual Evidence**Kun Xu, Siva Reddy, Yansong Feng, et al.*2016 · 357 citations*](https://md.research.elamur.ai/free/en/papers/xu-2016-question-answering-freebase-relation-extraction/)
35. [**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/)
36. [**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/)
37. [**ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers**Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.*2022 · 301 citations*](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/)
38. [**Modeling Biological Processes for Reading Comprehension**Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, et al.*2014 · 288 citations*](https://md.research.elamur.ai/free/en/papers/berant-2014-modeling-biological-processes-reading-comprehension/)
39. [**PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them**Patrick Lewis, Yuxiang Wu, Linqing Liu, et al.*2021 · 286 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2021-paq-65-million-probably-asked/)
40. [**Constructing Datasets for Multi-hop Reading Comprehension Across Documents**Johannes Welbl, Pontus Stenetorp, Sebastian Riedel*2018 · 283 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2018-constructing-datasets-multi-hop-reading/)
41. [**Generate rather than Retrieve: Large Language Models are Strong Context Generators**Wenhao Yu, Dan Iter, Shuohang Wang, et al.*2022 · 280 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-generate-rather-than-retrieve-large/)
42. [**BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models**Nandan Thakur, Nils Reimers, Andreas Rücklé, et al.*2021 · 262 citations*](https://md.research.elamur.ai/free/en/papers/thakur-2021-beir-heterogenous-benchmark-zero/)
43. [**DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale**Samyam Rajbhandari, Conglong Li, Zhewei Yao, et al.*2022 · 254 citations*](https://md.research.elamur.ai/free/en/papers/rajbhandari-2022-deepspeed-moe-advancing-mixture-experts/)
44. [**The Value of Semantic Parse Labeling for Knowledge Base Question Answering**Wen-tau Yih, Matthew Richardson, Chris Meek, et al.*2016 · 254 citations*](https://md.research.elamur.ai/free/en/papers/yih-2016-value-semantic-parse-labeling-knowledge/)
45. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 245 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
46. [**A Discrete Hard EM Approach for Weakly Supervised Question Answering**Sewon Min, Danqi Chen, Hannaneh Hajishirzi, et al.*2019 · 233 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-discrete-hard-em-approach-weakly/)
47. [**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/)
48. [**Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index**Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, et al.*2019 · 216 citations*](https://md.research.elamur.ai/free/en/papers/seo-2019-real-time-open-domain-question/)
49. [**Syntax for Semantic Role Labeling, To Be, Or Not To Be**Shexia He, Zuchao Li, Hai Zhao, et al.*2018 · 211 citations*](https://md.research.elamur.ai/free/en/papers/he-2018-syntax-semantic-role-labeling-be/)
50. [**Entities as Experts: Sparse Memory Access with Entity Supervision**Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, et al.*2020 · 205 citations*](https://md.research.elamur.ai/free/en/papers/fevry-2020-entities-as-experts-sparse-memory/)
51. [**Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base**Wen-tau Yih, Ming-Wei Chang, Xiaodong He, et al.*2015 · 203 citations*](https://md.research.elamur.ai/free/en/papers/yih-2015-semantic-parsing-staged-query-graph/)
52. [**A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/)
53. [**Improving Passage Retrieval with Zero-Shot Question Generation**Devendra Sachan, Mike Lewis, Mandar Joshi, et al.*2022 · 197 citations*](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/)
54. [**RealTime QA: What's the Answer Right Now?**Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al.*2022 · 176 citations*](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/)
55. [**A* CCG Parsing with a Supertag-factored Model**Mike Lewis, Mark Steedman*2014 · 175 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2014-ccg-parsing-supertag-factored-model/)
56. [**LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers**Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/)
57. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
58. [**Simple Entity-Centric Questions Challenge Dense Retrievers**Christopher Sciavolino, Zexuan Zhong, Jinhyuk Lee, et al.*2021 · 168 citations*](https://md.research.elamur.ai/free/en/papers/sciavolino-2021-simple-entity-centric-questions-challenge/)
59. [**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/)
60. [**Moshi: a speech-text foundation model for real-time dialogue**Alexandre Defossez, Laurent Mazare, Manu Orsini, et al.*2024 · 154 citations*](https://md.research.elamur.ai/free/en/papers/defossez-2024-moshi-speech-text-foundation-model/)
61. [**Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models**Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al.*2024 · 149 citations*](https://md.research.elamur.ai/free/en/papers/yu-2024-chain-note-enhancing-robustness-retrieval/)
62. [**RA-DIT: Retrieval-Augmented Dual Instruction Tuning**Xi Victoria Lin, Xilun Chen, Mingda Chen, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-ra-dit-retrieval-augmented-dual/)
63. [**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/)
64. [**Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering**Jinheon Baek, Alham Fikri Aji, Amir Saffari*2023 · 118 citations*](https://md.research.elamur.ai/free/en/papers/baek-2023-knowledge-augmented-language-model-prompting/)
65. [**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/)
66. [**IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions**Wenhao Yu, Meng Jiang, Peter Clark, et al.*2023 · 26 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-ifqa-dataset-open-domain-question/)
67. [**Distillation Scaling Laws**Dan Busbridge, Amitis Shidani, Floris Weers, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/busbridge-2025-distillation-scaling-laws/)
68. [**Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds**Igor Labutov, Bishan Yang, Anusha Prakash, et al.*2018 · 17 citations*](https://md.research.elamur.ai/free/en/papers/labutov-2018-multi-relational-question-answering-narratives/)
69. [**Cross-document Event Coreference Search: Task, Dataset and Modeling**Alon Eirew, Avi Caciularu, Ido Dagan*2022 · 15 citations*](https://md.research.elamur.ai/free/en/papers/eirew-2022-cross-document-event-coreference-search/)
70. [**Chronological Thinking in Full-Duplex Spoken Dialogue Language Models**Donghang Wu, Haoyang Zhang, Chen Chen, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/wu-2026-chronological-thinking-full-duplex-spoken/)
71. [**Evaluation Strategies for Computational Construction Grammars**Tânia Marques, Katrien Beuls*2016 · 1 citation*](https://md.research.elamur.ai/free/en/papers/marques-2016-evaluation-strategies-computational-construction-grammars/)
72. [**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/)
73. [**The UD-NewsCrawl Treebank: Reflections and Challenges from a Large-scale Tagalog Syntactic Annotation Project**Angelina Aspra Aquino, Lester James Validad Miranda, Elsie Marie T. Or*2025*](https://md.research.elamur.ai/free/en/papers/aquino-2025-ud-newscrawl-treebank-reflections-challenges/)
74. [**Overview on Knowledge Graph Embedding Technology Research (知识图谱嵌入技术研究综述)**Tiancheng Zhang (张天成), Xue Tian (田雪), Xianghui Sun (孙相会), et al.*2021*](https://md.research.elamur.ai/free/en/papers/zhang-2021-overview-knowledge-graph/)

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