---
title: "Who cites George E. Dahl — 595 papers of our library"
description: "595 papers of the Elamur Research library cite the works of George E. Dahl, found in their bibliographies; the most cited first."
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---
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# Who cites George E. Dahl

659 citations of their works from 595 papers of our library (of the 149,439 whose bibliographies we read) · 51 with a page here, the most cited first · [← George E. Dahl](https://md.research.elamur.ai/free/en/authors/george-e-dahl/)

1. [**A Comprehensive Survey on Graph Neural Networks arXiv:1901.00596**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,312 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/)
2. [**Neural Message Passing for Quantum Chemistry arXiv:1704.01212**Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, et al.*2017 · 3,793 citations*](https://md.research.elamur.ai/free/en/papers/gilmer-2017-neural-message-passing-quantum-chemistry/)
3. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
4. [**Relational inductive biases, deep learning, and graph networks arXiv:1806.01261**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,335 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
5. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
6. [**Fast Graph Representation Learning with PyTorch Geometric arXiv:1903.02428**Matthias Fey, Jan Eric Lenssen*2019 · 1,440 citations*](https://md.research.elamur.ai/free/en/papers/fey-2019-fast-graph-representation-learning-pytorch/)
7. **[Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-encoding-sentences-graph-convolutional-networks/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Diego Marcheggiani, Ivan Titov*2017 · 1,106 citations*
8. [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark arXiv:2311.12022**David Rein, Betty Li Hou, Asa Cooper Stickland, et al.*2023 · 1,018 citations*](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/)
9. [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
10. **[DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://md.research.elamur.ai/free/en/papers/ghosal-2019-dialoguegcn-graph-convolutional-neural-network/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Deepanway Ghosal, Navonil Majumder, Soujanya Poria, et al.*2019 · 853 citations*
11. [**SWE-bench: Can Language Models Resolve Real-World GitHub Issues? arXiv:2310.06770**Carlos E. Jimenez, John Yang, Alexander Wettig, et al.*2024 · 704 citations*](https://md.research.elamur.ai/free/en/papers/jimenez-2024-swe-bench-can-language-models/)
12. [**How Powerful are Graph Neural Networks? arXiv:1810.00826**Keyulu Xu, Weihua Hu, Jure Leskovec, et al.*2018 · 655 citations*](https://md.research.elamur.ai/free/en/papers/xu-2018-powerful-graph-neural-networks/)
13. **[Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://md.research.elamur.ai/free/en/papers/wang-2020-heterogeneous-graph-neural-networks-extractive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Danqing Wang, Pengfei Liu, Yining Zheng, et al.*2020 · 370 citations*
14. [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation arXiv:2201.05955**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 317 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)
15. **[Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://md.research.elamur.ai/free/en/papers/feng-2020-scalable-multi-hop-relational-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, et al.*2020 · 300 citations*
16. [**A Review of Large Language Models and Autonomous Agents in Chemistry arXiv:2407.01603**Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White*2024 · 288 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2024-review-large-language-models-autonomous/)
17. [**LexGLUE: A Benchmark Dataset for Legal Language Understanding in English arXiv:2110.00976**Ilias Chalkidis; Abhik Jana; Dirk Hartung; Michael Bommarito; Ion Androutsopoulos; Daniel Martin Katz;, Nikolaos Aletras*2021 · 258 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/)
18. **[Distinguish Confusing Law Articles for Legal Judgment Prediction](https://md.research.elamur.ai/free/en/papers/xu-2020-distinguish-confusing-law-articles-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nuo Xu, Pinghui Wang, Long Chen, et al.*2020 · 258 citations*
19. **[LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2022-lexglue-benchmark-dataset-legal-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Abhik Jana, Dirk Hartung, et al.*2022 · 239 citations*
20. **[Multimodal Transformer for Multimodal Machine Translation](https://md.research.elamur.ai/free/en/papers/yao-2020-multimodal-transformer-multimodal-machine-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shaowei Yao, Xiaojun Wan*2020 · 200 citations*
21. **[Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/bastings-2017-graph-convolutional-encoders-syntax-aware/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jasmijn Bastings, Ivan Titov, Wilker Aziz, et al.*2017 · 194 citations*
22. [**We Need to Consider Disagreement in Evaluation Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future**Valerio Basile, Michael Fell, Tommaso Fornaciari, et al.*2021 · 186 citations*](https://md.research.elamur.ai/free/en/papers/basile-2021-we-need-consider-disagreement-evaluation/)
23. **[QuALITY: Question Answering with Long Input Texts, Yes!](https://md.research.elamur.ai/free/en/papers/pang-2022-quality-question-answering-long-input/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.*2022 · 167 citations*
24. [**Neural Networks and the Chomsky Hierarchy arXiv:2207.02098**Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, et al.*2022 · 112 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2022-neural-networks-chomsky-hierarchy/)
25. **[AMR-To-Text Generation with Graph Transformer](https://md.research.elamur.ai/free/en/papers/wang-2020-amr-text-generation-graph-transformer/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tianming Wang, Xiaojun Wan, Hanqi Jin*2020 · 108 citations*
26. **[Graph-based Dependency Parsing with Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/ji-2019-graph-based-dependency-parsing-graph/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tao Ji, Yuanbin Wu, Man Lan*2019 · 94 citations*
27. **[Mapping global dynamics of benchmark creation and saturation in artificial intelligence](https://md.research.elamur.ai/free/en/papers/ott-2022-mapping-global-dynamics-benchmark-creation/) [Nature Communications](https://md.research.elamur.ai/free/en/venues/nature-communications/)**Simon Ott, Adriano Barbosa-Silva, Kathrin Blagec, et al.*2022 · 93 citations*
28. **[Improving Compositional Generalization with Latent Structure and Data Augmentation](https://md.research.elamur.ai/free/en/papers/qiu-2022-improving-compositional-generalization-latent-structure/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Linlu Qiu, Peter Shaw, Panupong Pasupat, et al.*2022 · 76 citations*
29. [**StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts**Zhengxiang Shi, Qiang Zhang, Aldo Lipani*2022 · 71 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-stepgame-new-benchmark-robust-multi/)
30. [**Language acquisition: do children and language models follow similar learning stages? arXiv:2306.03586**Linnea Evanson, Yair Lakretz, Jean-Rémi King*2023 · 53 citations*](https://md.research.elamur.ai/free/en/papers/evanson-2023-language-acquisition-children-language-models/)
31. **[VariErr NLI: Separating Annotation Error from Human Label Variation](https://md.research.elamur.ai/free/en/papers/genzel-2024-varierr-nli-separating-annotation-error/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leon Weber-Genzel, Siyao Peng, Marie-Catherine De Marneffe, et al.*2024 · 51 citations*
32. [**Auxiliary task demands mask the capabilities of smaller language models arXiv:2404.02418**Jennifer Hu, Michael C. Frank*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-auxiliary-task-demands-mask-capabilities/)
33. [**Introducing v0.5 of the AI Safety Benchmark from MLCommons arXiv:2404.12241**Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.*2024 · 38 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2024-introducing-v0-5-ai-safety/)
34. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)
35. **[Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://md.research.elamur.ai/free/en/papers/pyatkin-2023-design-choices-crowdsourcing-implicit-discourse/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Valentina Pyatkin, Frances Yung, Merel C. J. Scholman, et al.*2023 · 19 citations*
36. **[Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards](https://md.research.elamur.ai/free/en/papers/valentino-2021-do-natural-language-explanations-represent/) [International Conference on Computational Semantics](https://md.research.elamur.ai/free/en/venues/iwcs/)**Marco Valentino, Ian Pratt-Hartmann, André Freitas*2021 · 11 citations*
37. **[PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers](https://md.research.elamur.ai/free/en/papers/saxon-2023-peco-examining-single-sentence-label/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Michael Saxon, Xinyi Wang, Wenda Xu, et al.*2023 · 9 citations*
38. **[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*
39. [**Contrastive Learning of English Language and Crystal Graphs for Multimodal Representation of Materials Knowledge arXiv:2502.16451**Yang Jeong Park, Mayank Kumaran, Chia-Wei Hsu, et al.*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/park-2025-contrastive-learning-english-language-crystal/)
40. **[FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking](https://md.research.elamur.ai/free/en/papers/magomere-2025-finnli-novel-dataset-multi-genre/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jabez Magomere, Elena Kochkina, Samuel Mensah, et al.*2025 · 3 citations*
41. [**From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey arXiv:2108.11451**Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve, et al.*2021 · 3 citations*](https://md.research.elamur.ai/free/en/papers/marra-2021-statistical-relational-neurosymbolic-artificial/)
42. [**MÖVE: A Holistic LLM Benchmark for the German Public Sector arXiv:2606.13111**Camilla Dalerci, Thilo Michael, Robin Schaefer, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/dalerci-2026-m-ove-holistic-llm-benchmark/)
43. **[Towards a Diagnostic and Predictive Evaluation Methodology for Sequence Labeling Tasks](https://md.research.elamur.ai/free/en/papers/mellado-2026-diagnostic-predictive-evaluation-methodology-sequence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Elena Alvarez-Mellado, Julio Gonzalo*2026 · 1 citation*
44. **[Debiasing Logical Fallacy Detection for Real-World Robustness via Counterfactually Augmented Data](https://md.research.elamur.ai/free/en/papers/singh-2026-debiasing-logical-fallacy-detection-real/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Navyansh Singh*2026*
45. **[Graph-Based Alternatives to LLMs for Human Simulation](https://md.research.elamur.ai/free/en/papers/suh-2026-graph-based-alternatives-llms-human/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joseph Suh, Suhong Moon, Serina Chang*2026*
46. **[GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation](https://md.research.elamur.ai/free/en/papers/mu-2026-graphlora-structure-aware-low-rank/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Mu, Guoji Wang, Li Ni, et al.*2026*
47. **[Linguistic Initialization for Inductive Reasoning in Heterogeneous Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/pasquini-2026-linguistic-initialization-inductive-reasoning-heterogeneous/) [Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26](https://md.research.elamur.ai/free/en/venues/kallm/)**Daniele Pasquini, Danilo Croce, Roberto Basili*2026*
48. **[MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction](https://md.research.elamur.ai/free/en/papers/pu-2026-mcle-mol-empowering-llm-molecular/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhili Pu, Lantian Zhang, Hao Duan, et al.*2026*
49. **[MuSe: Multi-Stage Graph Reasoning via Vision-Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-muse-multi-stage-graph-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanyu Wang, Xu Chu, Zhijie Tan, et al.*2026*
50. **[Methods for Constraint Solving Problems Based on Learn to Reason Model: A Review (基于学习-推理的约束求解方法研究进展)](https://md.research.elamur.ai/free/en/papers/zou-2023-methods-constraint-solving/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yue Zou (邹悦), Jiayang Lai (赖家洋), Yonggang Zhang (张永刚)*2023*
51. **[Overview on Knowledge Graph Embedding Technology Research (知识图谱嵌入技术研究综述)](https://md.research.elamur.ai/free/en/papers/zhang-2021-overview-knowledge-graph/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Tiancheng Zhang (张天成), Xue Tian (田雪), Xianghui Sun (孙相会), et al.*2021*

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