---
title: "Who cites Lizhen Qu — 204 papers of our library"
description: "204 papers of the Elamur Research library cite the works of Lizhen Qu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/lizhen-qu/cited-by/"
---
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# Who cites Lizhen Qu

211 citations of their works from 204 papers of our library (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← Lizhen Qu](https://md.research.elamur.ai/free/en/authors/lizhen-qu/)

1. [**RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space arXiv:1902.10197**Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, et al.*2019 · 1,073 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-rotate-knowledge-graph-embedding-relational/)
2. [**TuckER: Tensor Factorization for Knowledge Graph Completion arXiv:1901.09590**Ivana Balažević, Carl Allen, Timothy M. Hospedales*2019 · 799 citations*](https://md.research.elamur.ai/free/en/papers/balazevic-2019-tucker-tensor-factorization-knowledge-graph/)
3. **[The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://md.research.elamur.ai/free/en/papers/pilan-2022-text-anonymization-benchmark-tab-dedicated/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Ildikó Pilán, Pierre Lison, Lilja Øvrelid, et al.*2022 · 131 citations*
4. **[Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://md.research.elamur.ai/free/en/papers/eger-2025-transforming-science-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Steffen Eger, Yong Cao, Jennifer D'Souza, et al.*2025 · 25 citations*
5. **[Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models](https://md.research.elamur.ai/free/en/papers/kim-2024-developing-pragmatic-benchmark-assessing-korean/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yeeun Kim, Youngrok Choi, Eunkyung Choi, et al.*2024 · 24 citations*
6. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
7. [**Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment arXiv:2411.03417**Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/goldberg-2024-usefulness-llms-author-checklist-assistant/)
8. **[Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews](https://md.research.elamur.ai/free/en/papers/vasu-2026-justice-judgment-unveiling-hidden-bias/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sai Suresh Macharla Vasu, Ivaxi Sheth, Hui-Po Wang, et al.*2026 · 8 citations*
9. [**Reviewing Scientific Papers for Critical Problems With Reasoning LLMs: Baseline Approaches and Automatic Evaluation arXiv:2505.23824**Tianmai M. Zhang, Neil F. Abernethy*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-reviewing-scientific-papers-critical-problems/)
10. **[Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework](https://md.research.elamur.ai/free/en/papers/dycke-2026-automatic-reviewers-fail-detect-faulty/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nils Dycke, Iryna Gurevych*2026 · 5 citations*
11. **[NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment](https://md.research.elamur.ai/free/en/papers/wu-2026-novbench-evaluating-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenqing Wu, Yi Zhao, Yuzhuo Wang, et al.*2026 · 4 citations*
12. **[Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review](https://md.research.elamur.ai/free/en/papers/ruan-2026-author-loop-response-generation-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qian Ruan, Iryna Gurevych*2026 · 2 citations*
13. **[Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue](https://md.research.elamur.ai/free/en/papers/purkayastha-2026-decision-making-deliberation-meta-reviewing/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sukannya Purkayastha, Nils Dycke, Anne Lauscher, et al.*2026 · 2 citations*
14. **[How well ChatGPT understand Malaysian English? An Evaluation on Named Entity Recognition and Relation Extraction](https://md.research.elamur.ai/free/en/papers/chanthran-2023-how-well-chatgpt-understand-malaysian/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Mohan Raj Chanthran, Lay-Ki Soon, Huey Fang Ong, et al.*2023 · 2 citations*
15. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
16. **[Federated LoRA Fine-Tuning with Pipelined Error-Mitigated Aggregation and Matrix-Wise Freezing](https://md.research.elamur.ai/free/en/papers/wang-2026-federated-lora-fine-tuning-pipelined/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoran Wang, Xiong Wang, Yuqing Li, et al.*2026*
17. **[FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation](https://md.research.elamur.ai/free/en/papers/siddika-2026-fedreft-federated-representation-fine-tuning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fatema Siddika, Md Anwar Hossen, Juan Pablo Munoz, et al.*2026*
18. **[Implicit Bias in Peer Review: Through the Lens of Language Abstraction](https://md.research.elamur.ai/free/en/papers/zhang-2026-implicit-bias-peer-review-through/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Xulang Zhang, Rui Mao, Erik Cambria*2026*
19. **[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*
20. **[The Double Bind: Revisiting Preprinting and Peer Review Two Years After the Removal of the ACL Anonymity Period](https://md.research.elamur.ai/free/en/papers/pranav-2026-double-bind-revisiting-preprinting-peer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**A Pranav, Shane Storks, Anne Lauscher*2026*
21. **[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*

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

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