🧑‍🔬 Who cites Ming Zhu

119 citations of their works from 114 papers of our library (of the 149,439 whose bibliographies we read) · 30 with a page here, the most cited first · ← Ming Zhu

1. Crosslingual Generalization through Multitask FinetuningarXiv:2211.01786Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.2022 · 703 citations
2. CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-XACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)Qinkai Zheng, Xiao Xia, Xu Zou, et al.2023 · 465 citations
3. Aya Model: An Instruction Finetuned Open-Access Multilingual Language ModelarXiv:2402.07827Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.2024 · 253 citations
4. CRUXEval: A Benchmark for Code Reasoning, Understanding and ExecutionarXiv:2401.03065Alex Gu, Baptiste Rozière, Hugh Leather, et al.2024 · 122 citations
5. OS-ATLAS: A Foundation Action Model for Generalist GUI AgentsarXiv:2410.23218Zhiyong Wu, Zhenyu Wu, Fangzhi Xu, et al.2024 · 67 citations
6. XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and RetrievalAnnual Meeting of the Association for Computational Linguistics (ACL)Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.2024 · 51 citations
7. xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and RetrievalarXiv:2303.03004Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.2023 · 42 citations
8. NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning ModelarXiv:2508.14444NVIDIA: Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, et al.2025 · 39 citations
9. APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human InterplayConference on Neural Information Processing Systems (NeurIPS)Akshara Prabhakar, Zuxin Liu, Ming Zhu, et al.2025 · 36 citations
10. MMLU-CF: A Contamination-free Multi-task Language Understanding BenchmarkAnnual Meeting of the Association for Computational Linguistics (ACL)Qihao Zhao, Yangyu Huang, Tengchao Lv, et al.2025 · 21 citations
11. NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API CallsConference on Empirical Methods in Natural Language Processing (EMNLP)Kinjal Basu, Ibrahim Abdelaziz, Kiran Kate, et al.2025 · 16 citations
12. Qwen3-Coder-Next Technical ReportarXiv:2603.00729Ruisheng Cao, Mouxiang Chen, Jiawei Chen, et al.2026 · 15 citations
13. Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language ModelAnnual Meeting of the Association for Computational Linguistics (ACL)Emre Can Acikgoz, Jeremiah Greer, Akul Datta, et al.2025 · 13 citations
14. When2Call: When (not) to Call ToolsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Hayley Ross, Ameya Sunil Mahabaleshwarkar, Yoshi Suhara2025 · 11 citations
15. LoopTool: Closing the Data–Training Loop for Robust LLM Tool CallsAnnual Meeting of the Association for Computational Linguistics (ACL)Kangning Zhang, Weiwen Liu, Wenxiang Jiao, et al.2026 · 6 citations
16. rStar2-Agent: Agentic Reasoning Technical ReportarXiv:2508.20722Ning Shang, Yifei Liu, Yi Zhu, et al.2025 · 6 citations
17. EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic SynthesisFindings of the Association for Computational LinguisticsXiaoshuai Song, Haofei Chang, Guanting Dong, et al.2026 · 5 citations
18. Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from TextAnnual Meeting of the Association for Computational Linguistics (ACL)Zhihao Xu, Rumei Li, Jiahuan Li, et al.2026 · 4 citations
19. Failure makes the agent stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool InteractionsFindings of the Association for Computational LinguisticsJunhao Su, Yuanliang Wan, Junwei Yang, et al.2026 · 2 citations
20. Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User IntentsAnnual Meeting of the Association for Computational Linguistics (ACL)Ziyi Wang, Yuxuan Lu, Yimeng Zhang, et al.2026 · 2 citations
21. DRAGON: Domain-specific Robust Automatic Data Generation for RAG OptimizationFindings of the Association for Computational LinguisticsHaiyang Shen, Hang Yan, Zhongshi Xing, et al.2026 · 1 citation
22. Label-Consistent Data Generation for Aspect-Based Sentiment Analysis Using LLM AgentsWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisMohammad Hossein Akbari Monfared, Lucie Flek, Akbar Karimi2026 · 1 citation
23. Robust Tool Use via Fission-GRPO: Learning to Recover from Execution ErrorsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiwei Zhang, Fei Zhao, Rui Wang, et al.2026 · 1 citation
24. SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMsFindings of the Association for Computational LinguisticsJie Sun, Yu Liu, Lu Han, et al.2026 · 1 citation
25. TInR: Exploring Tool-Internalized Reasoning in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Qiancheng Xu, Yongqi Li, Fan Liu, et al.2026 · 1 citation
26. Deep Learning Based Code Generation Methods: Literature Review (基于深度学习的代码生成方法研究进展)Journal of SoftwareZezhou Yang (杨泽洲), Sirong Chen (陈思榕), Cuiyun Gao (高翠芸), et al.2023 · 1 citation
27. EfficientTool: A Cost-Effective Aligning Framework for Tool-Conditioned Agents in SME ScenariosAnnual Meeting of the Association for Computational Linguistics (ACL)Yuanqi Mu, Bingfeng Pi, Defei Xia, et al.2026
28. FAER: Benchmarking VLMs for Failure-Aware Embodied ReasoningFindings of the Association for Computational LinguisticsHao Song, Kaifeng Liu, Yuanxing Liu, et al.2026
29. Training Verifier to Assessing Complex Real-World Tool-Use TrajectoriesFindings of the Association for Computational LinguisticsLinzhuang Sun, Mingyang Chen, Hao Liang, et al.2026
30. U-Fold: Dynamic Intent-Aware Context Folding for User-Centric AgentsFindings of the Association for Computational LinguisticsJin Su, Runnan Fang, Yeqiu Li, et al.2026

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