--- title: "Who cites Wei Zhu — 104 papers of our library" description: "104 papers of the Elamur Research library cite the works of Wei Zhu, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/wei-zhu/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/wei-zhu/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Wei Zhu](https://md.research.elamur.ai/free/en/authors/wei-zhu/) / cited by # Who cites Wei Zhu 105 citations of their works from 104 papers of our library (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← Wei Zhu](https://md.research.elamur.ai/free/en/authors/wei-zhu/) 1. **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,010 citations* 2. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/) 3. [**The Prompt Report: A Systematic Survey of Prompt Engineering Techniques arXiv:2406.06608**Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.*2024 · 189 citations*](https://md.research.elamur.ai/free/en/papers/schulhoff-2024-prompt-report-systematic-survey-prompt/) 4. **[Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning](https://md.research.elamur.ai/free/en/papers/wang-2023-label-words-anchors-information-flow/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lean Wang, Lei Li, Damai Dai, et al.*2023 · 153 citations* 5. **[Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://md.research.elamur.ai/free/en/papers/shao-2024-assisting-writing-wikipedia-like-articles/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yijia Shao, Yucheng Jiang, Theodore Kanell, et al.*2024 · 131 citations* 6. [**Efficient Large Language Models: A Survey arXiv:2312.03863**Zhongwei Wan, Xin Wang, Che Liu, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-efficient-large-language-models-survey/) 7. **[Learning to Retrieve In-Context Examples for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-learning-retrieve-context-examples-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Liang Wang, Nan Yang, Furu Wei*2024 · 106 citations* 8. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/) 9. **[Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Shangyu Wu, Ying Xiong, Yufei Cui, et al.*2024 · 86 citations* 10. [**Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks arXiv:2310.10844**Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.*2023 · 83 citations*](https://md.research.elamur.ai/free/en/papers/shayegani-2023-survey-vulnerabilities-large-language-models/) 11. [**In-context Learning with Retrieved Demonstrations for Language Models: A Survey arXiv:2401.11624**Man Luo, Xin Xu, Yue Liu, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-context-learning-retrieved-demonstrations-language/) 12. **[Knowledge Boundary of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/li-2025-knowledge-boundary-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Moxin Li, Yong Zhao, Wenxuan Zhang, et al.*2025 · 30 citations* 13. [**DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning arXiv:2310.02954**Jing Xiong, Zixuan Li, Chuanyang Zheng, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2023-dq-lore-dual-queries-low/) 14. **[Teaching Small Language Models Reasoning through Counterfactual Distillation](https://md.research.elamur.ai/free/en/papers/feng-2024-teaching-small-language-models-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tao Feng, Yicheng Li, Chenglin Li, et al.*2024 · 21 citations* 15. **[AttnPO: Attention-Guided Process Supervision for Efficient Reasoning](https://md.research.elamur.ai/free/en/papers/nie-2026-attnpo-attention-guided-process-supervision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuaiyi Nie, Dingsiyu, Wenyuan Zhang, et al.*2026 · 18 citations* 16. **[Automatic Combination of Sample Selection Strategies for Few-Shot Learning](https://md.research.elamur.ai/free/en/papers/pecher-2026-automatic-combination-sample-selection-strategies/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Branislav Pecher, Ivan Srba, Maria Bielikova, et al.*2026 · 15 citations* 17. **[Understanding LLM Reasoning for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/yuan-2026-understanding-llm-reasoning-abstractive-summarization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haohan Yuan, Haopeng Zhang*2026 · 11 citations* 18. **[Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2025-unraveling-mechanics-learning-based-demonstration/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Liu, Wenya Wang, Hao Sun, et al.*2025 · 11 citations* 19. [**Unearthing Large Scale Domain-Specific Knowledge from Public Corpora arXiv:2401.14624**Zhaoye Fei, Yunfan Shao, Linyang Li, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/fei-2024-unearthing-large-scale-domain-specific/) 20. **[Confidence-Aware Ranker Ensembles for Robust In-Context Knowledge Editing](https://md.research.elamur.ai/free/en/papers/nair-2026-confidence-aware-ranker-ensembles-robust/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tejal Nair, Mahmud Wasif Nafee, Maiqi Jiang, et al.*2026* 21. **[Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval](https://md.research.elamur.ai/free/en/papers/xu-2026-robust-context-learning-leveraging-out/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Xu, Rite Bo, Fausto Giunchiglia, et al.*2026* 83 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/wei-zhu/) — Wei Zhu