---
title: "Who cites Jeffrey Ling — 379 papers of our library"
description: "379 papers of the Elamur Research library cite the works of Jeffrey Ling, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/jeffrey-ling/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jeffrey-ling/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Jeffrey Ling](https://md.research.elamur.ai/free/en/authors/jeffrey-ling/) / cited by

# Who cites Jeffrey Ling

380 citations of their works from 379 papers of our library (of the 149,439 whose bibliographies we read) · 42 with a page here, the most cited first · [← Jeffrey Ling](https://md.research.elamur.ai/free/en/authors/jeffrey-ling/)

1. [**Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing arXiv:2007.15779**Yu Gu; Robert Tinn; Hao Cheng; Michael Lucas; Naoto Usuyama; Xiaodong Liu; Tristan Naumann; Jianfeng Gao;, Hoifung Poon*2020 · 2,674 citations*](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
2. **[SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,616 citations*
3. **[OpenNMT: Open-Source Toolkit for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/klein-2017-opennmt-open-source-toolkit-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guillaume Klein, Yoon Kim, Yuntian Deng, et al.*2017 · 2,475 citations*
4. **[UNITER: UNiversal Image-TExt Representation Learning](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yen-Chun Chen, Linjie Li, Licheng Yu, et al.*2019 · 2,418 citations*
5. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
6. **[Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,556 citations*
7. **[Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) [IEEE Transactions on Knowledge and Data Engineering](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-knowledge-and-data-engineering/)**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,551 citations*
8. **[How Can We Know What Language Models Know?](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zhengbao Jiang, Frank F. Xu, Jun Araki, et al.*2020 · 1,521 citations*
9. [**VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530**Weijie Su, Xizhou Zhu, Yue Cao, et al.*2019 · 1,066 citations*](https://md.research.elamur.ai/free/en/papers/su-2019-vl-bert-pre-training-generic/)
10. [**K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters arXiv:2002.01808**Ruize Wang, Duyu Tang, Nan Duan, et al.*2020 · 666 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-k-adapter-infusing-knowledge-pre/)
11. **[A Frustratingly Easy Approach for Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zexuan Zhong, Danqi Chen*2020 · 631 citations*
12. **[HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://md.research.elamur.ai/free/en/papers/li-2020-hero-hierarchical-encoder-video-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Linjie Li, Yen-Chun Chen, Yu Cheng, et al.*2020 · 481 citations*
13. **[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://md.research.elamur.ai/free/en/papers/srinivasan-2021-wit-wikipedia-based-image-text/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Krishna Srinivasan, Karthik Raman, Jiecao Chen, et al.*2021 · 315 citations*
14. **[Double Graph Based Reasoning for Document-level Relation Extraction](https://md.research.elamur.ai/free/en/papers/zeng-2020-double-graph-based-reasoning-document/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shuang Zeng, Runxin Xu, Baobao Chang, et al.*2020 · 314 citations*
15. [**The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) arXiv:2309.17421**Zhengyuan Yang, Linjie Li, Kevin Lin, et al.*2023 · 311 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-dawn-lmms-preliminary-explorations-gpt/)
16. **[Learning from Context or Names? An Empirical Study on Neural Relation Extraction](https://md.research.elamur.ai/free/en/papers/peng-2020-learning-context-or-names-empirical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hao Peng, Tianyu Gao, Xu Han, et al.*2020 · 255 citations*
17. **[GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://md.research.elamur.ai/free/en/papers/wan-2023-gpt-re-context-learning-relation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Wan, Fei Cheng, Zhuoyuan Mao, et al.*2023 · 253 citations*
18. **[TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task](https://md.research.elamur.ai/free/en/papers/alt-2020-tacred-revisited-thorough-evaluation-tacred/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig*2020 · 213 citations*
19. **[Entities as Experts: Sparse Memory Access with Entity Supervision](https://md.research.elamur.ai/free/en/papers/fevry-2020-entities-as-experts-sparse-memory/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, et al.*2020 · 208 citations*
20. **[Packed Levitated Marker for Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/ye-2022-packed-levitated-marker-entity-relation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Deming Ye, Yankai Lin, Peng Li, et al.*2022 · 189 citations*
21. **[Prompt-learning for Fine-grained Entity Typing](https://md.research.elamur.ai/free/en/papers/ding-2022-prompt-learning-fine-grained-entity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ning Ding, Yulin Chen, Xu Han, et al.*2022 · 181 citations*
22. **[Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/tan-2022-document-level-relation-extraction-adaptive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qingyu Tan, Ruidan He, Lidong Bing, et al.*2022 · 169 citations*
23. **[A Survey of Machine Learning for Big Code and Naturalness](https://md.research.elamur.ai/free/en/papers/allamanis-2017-survey-machine-learning-big-code/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, et al.*2017 · 167 citations*
24. [**Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding arXiv:2210.03347**Kenton Lee, Mandar Joshi, Iulia Turc, et al.*2022 · 158 citations*](https://md.research.elamur.ai/free/en/papers/lee-2022-pix2struct-screenshot-parsing-pretraining-visual/)
25. **[CLEVE: Contrastive Pre-training for Event Extraction](https://md.research.elamur.ai/free/en/papers/wang-2021-cleve-contrastive-pre-training-event/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziqi Wang, Xiaozhi Wang, Xu Han, et al.*2021 · 155 citations*
26. **[Continual Relation Learning via Episodic Memory Activation and Reconsolidation](https://md.research.elamur.ai/free/en/papers/han-2020-continual-relation-learning-episodic-memory/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xu Han, Yi Dai, Tianyu Gao, et al.*2020 · 152 citations*
27. **[ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning](https://md.research.elamur.ai/free/en/papers/qin-2021-erica-improving-entity-relation-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yujia Qin, Yankai Lin, Ryuichi Takanobu, et al.*2021 · 139 citations*
28. **[Evidence-based Fact-Checking of Health-related Claims](https://md.research.elamur.ai/free/en/papers/sarrouti-2021-evidence-based-fact-checking-health/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mourad Sarrouti, Asma Ben Abacha, Yassine Mrabet, et al.*2021 · 129 citations*
29. **[An Improved Baseline for Sentence-level Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhou-2022-improved-baseline-sentence-level-relation/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Wenxuan Zhou, Muhao Chen*2022 · 124 citations*
30. [**Nougat: Neural Optical Understanding for Academic Documents arXiv:2308.13418**Lukas Blecher, Guillem Cucurull, Thomas Scialom, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/blecher-2023-nougat-neural-optical-understanding-academic/)
31. [**Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data Proceedings of the First International Workshop on Natural Language Processing Beyond Text**Xinyu Wang, Xiaowen Sun, Tan Yang, et al.*2020 · 62 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-building-bridge-method-image-text/)
32. [**The Life Cycle of Knowledge in Big Language Models: A Survey arXiv:2303.07616 · Machine Intelligence Research. vol. 21, no. 2, pp. 217-238, 2024**Boxi Cao, Hongyu Lin, Xianpei Han, et al.*2023 · 31 citations*](https://md.research.elamur.ai/free/en/papers/cao-2023-life-cycle-knowledge-big-language/)
33. [**ConPET: Continual Parameter-Efficient Tuning for Large Language Models arXiv:2309.14763**Chenyang Song, Xu Han, Zheni Zeng, et al.*2023 · 14 citations*](https://md.research.elamur.ai/free/en/papers/song-2023-conpet-continual-parameter-efficient-tuning/)
34. [**Computational Sarcasm Analysis on Social Media: A Systematic Review arXiv:2209.06170**Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, et al.*2022 · 4 citations*](https://md.research.elamur.ai/free/en/papers/kader-2022-computational-sarcasm-analysis-social-media/)
35. [**Temporal Relation Classification in Hebrew**Guy Yanko, Shahaf Pariente, Kfir Bar*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/yanko-2023-temporal-relation-classification-hebrew/)
36. **[RES2 at IMPOLS 2026: Implicit Content Classification via Span-Awareness, Hierarchical Multi-Task Learning and Synthetic Data](https://md.research.elamur.ai/free/en/papers/spezia-2026-res2-impols-2026-implicit-content/) [Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop](https://md.research.elamur.ai/free/en/venues/evalita/)**Salvatore Spezia, Salvatore Ferrara, Roberto Dioguardi, et al.*2026 · 1 citation*
37. **[A Study on Building Efficient Zero-Shot Relation Extraction Models](https://md.research.elamur.ai/free/en/papers/thomas-2026-study-building-efficient-zero-shot/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hugo THOMAS, Caio Corro, Guillaume Gravier, et al.*2026*
38. **[Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations](https://md.research.elamur.ai/free/en/papers/mtumbuka-2026-contrastively-pre-trained-event-embeddings/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Frank Mtumbuka, Steven Schockaert*2026*
39. **[Datasets for Scientific Literature Understanding: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2026-datasets-scientific-literature-understanding-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuanzhe Zhang, Xun Zhao, Maodi Hu, et al.*2026*
40. **[Do Syntactic Features Help Biomedical Relation Extraction? An Empirical Study of Verb Token and Dependency Graph Augmentation](https://md.research.elamur.ai/free/en/papers/sikder-2026-do-syntactic-features-help-biomedical/) [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/)**Mustafa Sikder, Ernest Kwegyir-Afful*2026*
41. **[Tracing Relational Knowledge Recall in Large Language Models](https://md.research.elamur.ai/free/en/papers/popovic-2026-tracing-relational-knowledge-recall-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nicholas Popovič, Michael Färber*2026*
42. **[Survey on Vision-language Pre-training (视觉语言预训练综述)](https://md.research.elamur.ai/free/en/papers/yin-2022-vision-language-pre/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Jiong Yin (殷炯), Zhedong Zhang (张哲东), Yuhan Gao (高宇涵), et al.*2022*

337 more citing papers of our library have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/authors/jeffrey-ling/) — Jeffrey Ling