---
title: "Who cites Mengling Feng — 598 papers of our library"
description: "598 papers of the Elamur Research library cite the works of Mengling Feng, found in their bibliographies; the most cited first."
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---
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# Who cites Mengling Feng

604 citations of their works from 598 papers of our library (of the 141,840 whose bibliographies we read) · 23 with a page here, the most cited first · [← Mengling Feng](https://md.research.elamur.ai/free/en/authors/mengling-feng/)

1. **[SciBERT: A Pretrained Language Model for Scientific Text](https://md.research.elamur.ai/free/en/papers/beltagy-2019-scibert-pretrained-language-model-scientific/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Iz Beltagy, Kyle Lo, Arman Cohan*2019 · 3,567 citations*
2. **[MIMIC-IV, a freely accessible electronic health record dataset](https://md.research.elamur.ai/free/en/papers/johnson-2023-mimic-iv-freely-accessible-electronic/) [Scientific Data](https://md.research.elamur.ai/free/en/venues/scientific-data/)**Alistair E. W. Johnson, Lucas Bulgarelli, Lu Shen, et al.*2023 · 3,386 citations*
3. [**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 · 2,655 citations*](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
4. [**Publicly Available Clinical BERT Embeddings arXiv:1904.03323**Emily Alsentzer, John R. Murphy, Willie Boag, et al.*2019 · 1,226 citations*](https://md.research.elamur.ai/free/en/papers/alsentzer-2019-publicly-available-clinical-bert-embeddings/)
5. **[Attention is not not Explanation](https://md.research.elamur.ai/free/en/papers/wiegreffe-2019-attention-is-not-not-explanation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sarah Wiegreffe, Yuval Pinter*2019 · 1,196 citations*
6. **[Attention is not Explanation](https://md.research.elamur.ai/free/en/papers/jain-2019-attention-is-not-explanation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Sarthak Jain, Byron C. Wallace*2019 · 1,005 citations*
7. [**ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission arXiv:1904.05342**Kexin Huang, Jaan Altosaar, Rajesh Ranganath*2019 · 798 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-clinicalbert-modeling-clinical-notes-predicting/)
8. **[Explainable Prediction of Medical Codes from Clinical Text](https://md.research.elamur.ai/free/en/papers/mullenbach-2018-explainable-prediction-medical-codes-clinical/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**James Mullenbach, Sarah Wiegreffe, Jon Duke, et al.*2018 · 719 citations*
9. **[Joint Embedding of Words and Labels for Text Classification](https://md.research.elamur.ai/free/en/papers/wang-2018-joint-embedding-words-labels-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guoyin Wang, Chunyuan Li, Wenlin Wang, et al.*2018 · 464 citations*
10. **[Large language models are few-shot clinical information extractors](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.*2022 · 450 citations*
11. **[BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://md.research.elamur.ai/free/en/papers/labrak-2024-biomistral-collection-open-source-pretrained/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yanis Labrak, Adrien Bazoge, Emmanuel Morin, et al.*2024 · 408 citations*
12. **[Lessons from Natural Language Inference in the Clinical Domain](https://md.research.elamur.ai/free/en/papers/romanov-2018-lessons-natural-language-inference-clinical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexey Romanov, Chaitanya Shivade*2018 · 348 citations*
13. **[Ethical Research Protocols for Social Media Health Research](https://md.research.elamur.ai/free/en/papers/benton-2017-ethical-research-protocols-social-media/) [ACL Workshop on Ethics in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/acl-workshop-on-ethics-in-natural-language-processing/)**Adrian Benton, Glen Coppersmith, Mark Dredze*2017 · 292 citations*
14. **[emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://md.research.elamur.ai/free/en/papers/pampari-2018-emrqa-large-corpus-question-answering/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Anusri Pampari, Preethi Raghavan, Jennifer Liang, et al.*2018 · 288 citations*
15. **[Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces](https://md.research.elamur.ai/free/en/papers/rios-2018-few-shot-zero-shot-multi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Anthony Rios, Ramakanth Kavuluru*2018 · 246 citations*
16. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
17. **[CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters](https://md.research.elamur.ai/free/en/papers/boukkouri-2020-characterbert-reconciling-elmo-bert-word/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Hicham El Boukkouri, Olivier Ferret, Thomas Lavergne, et al.*2020 · 204 citations*
18. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 153 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
19. [**A Survey of Large Language Models in Medicine: Progress, Application, and Challenge arXiv:2311.05112**Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/)
20. **[MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain](https://md.research.elamur.ai/free/en/papers/herlihy-2021-mednli-is-not-immune-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Christine Herlihy, Rachel Rudinger*2021 · 30 citations*
21. **[J-ClinicalBench: A Benchmark for Evaluating Large Language Models on Practical Clinical Tasks in Japanese](https://md.research.elamur.ai/free/en/papers/shimizu-2026-j-clinicalbench-benchmark-evaluating-large/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Seiji Shimizu, Tomohiro Nishiyama, HISADA Shohei, et al.*2026*
22. **[Persona-Conditioned Generation of Patient Self-Reports from EHRs](https://md.research.elamur.ai/free/en/papers/wu-2026-persona-conditioned-generation-patient-self/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yuexin Wu, Jianming Wei, Vasile Rus*2026*
23. [**Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health care arXiv:2406.15966**Hassan Alhuzali, Ashwag Alasmari*2024*](https://md.research.elamur.ai/free/en/papers/alhuzali-2024-evaluating-effectiveness-foundational-models-q/)

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

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