--- title: "Who cites Hangxiao Zhu — 108 papers of our library" description: "108 papers of the Elamur Research library cite the works of Hangxiao Zhu, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/hangxiao-zhu/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/hangxiao-zhu/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Hangxiao Zhu](https://md.research.elamur.ai/free/en/authors/hangxiao-zhu/) / cited by # Who cites Hangxiao Zhu 108 citations of their works from 108 papers of our library (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · [← Hangxiao Zhu](https://md.research.elamur.ai/free/en/authors/hangxiao-zhu/) 1. **[On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://md.research.elamur.ai/free/en/papers/long-2024-llms-driven-synthetic-data-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Long, Rui Wang, Ruixuan Xiao, et al.*2024 · 237 citations* 2. **[What is the Role of Small Models in the LLM Era: A Survey](https://md.research.elamur.ai/free/en/papers/chen-2024-role-small-models-llm-era/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Lihu Chen, Gael Varoquaux*2024 · 23 citations* 3. **[Label-Consistent Data Generation for Aspect-Based Sentiment Analysis Using LLM Agents](https://md.research.elamur.ai/free/en/papers/monfared-2026-label-consistent-data-generation-aspect/) [Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis](https://md.research.elamur.ai/free/en/venues/wassa/)**Mohammad Hossein Akbari Monfared, Lucie Flek, Akbar Karimi*2026 · 1 citation* 4. **[SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models](https://md.research.elamur.ai/free/en/papers/nghiem-2026-smarter-data-efficient-framework-improve/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huy Nghiem, Advik Sachdeva, Hal Daumé Iii*2026 · 1 citation* 5. **[A Novel Synthetic Dataset for Few-Shot Legal Relation Extraction in German](https://md.research.elamur.ai/free/en/papers/nouri-2026-novel-synthetic-dataset-few-shot/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Shiva Banasaz Nouri, Elena Leitner, Julian Moreno-Schneider, et al.*2026* 6. **[Entity Linking for Faroese Using Large Language Models with Web Search](https://md.research.elamur.ai/free/en/papers/simonsen-2026-entity-linking-faroese-large-language/) [Workshop on the Role of Resources in the Age of Large Language Models](https://md.research.elamur.ai/free/en/venues/resourceful/)**Annika Simonsen, Iben Nyholm Debess, Hafsteinn Einarsson*2026* 7. **[Generation of Instruction and Preference Dataset for Improving Japanese Instruction Following in LLMs](https://md.research.elamur.ai/free/en/papers/moriyama-2026-generation-instruction-preference-dataset-improving/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kei Moriyama, Takashi Kodama, Kouta Nakayama*2026* 8. [**Hate Speech Detection in Turkish and Arabic: A Comprehensive Study arXiv:2607.00143**Somaiyeh Dehghan, Gokçe Uludo\ugan, Mehmet Umut Şen, et al.*2026*](https://md.research.elamur.ai/free/en/papers/dehghan-2026-hate-speech-detection-turkish-arabic/) 9. **[ID10M-JAM: Stress-Testing Idiom Identification Under Challenging Context](https://md.research.elamur.ai/free/en/papers/hashiloni-2026-id10m-jam-stress-testing-idiom/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Golan Hashiloni, Lior Livyatan, Ofri Hefetz, et al.*2026* 10. **[Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions](https://md.research.elamur.ai/free/en/papers/zhao-2026-large-language-models-are-bad/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minda Zhao, Yilun Du, Mengyu Wang*2026* 11. **[Huaputong: Large Language Model for Genealogical Question-answering with Knowledge Reasoning (华谱通: 基于知识推理的家谱问答大语言模型)](https://md.research.elamur.ai/free/en/papers/wu-2025-huaputong-large-language/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Xindong Wu (吴信东), Xingrui Zhuo (卓兴锐), Yongpan Chang (常永泮), et al.*2025* 97 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/hangxiao-zhu/) — Hangxiao Zhu