| Alpagasus: Training a better alpaca with fewer data L Chen, S Li, J Yan, H Wang, K Gunaratna, V Yadav, Z Tang, V Srinivasan, ... arXiv preprint arXiv:2307.08701, 2023 | 228 | 2023 |
| Backdooring instruction-tuned large language models with virtual prompt injection J Yan, V Yadav, S Li, L Chen, Z Tang, H Wang, V Srinivasan, X Ren, H Jin arXiv preprint arXiv:2307.16888, 2023 | 103* | 2023 |
| Eidos, INDRA, & Delphi: from free text to executable causal models R Sharp, A Pyarelal, B Gyori, K Alcock, E Laparra, ... Proceedings of the 2019 conference of the north American chapter of the …, 2019 | 31 | 2019 |
| Instruction-following evaluation through verbalizer manipulation S Li, J Yan, H Wang, Z Tang, X Ren, V Srinivasan, H Jin arXiv preprint arXiv:2307.10558, 2023 | 22 | 2023 |
| How may i help you? using neural text simplification to improve downstream nlp tasks H Van, Z Tang, M Surdeanu arXiv preprint arXiv:2109.04604, 2021 | 20 | 2021 |
| Exploring interpretability in event extraction: Multitask learning of a neural event classifier and an explanation decoder Z Tang, G Hahn-Powell, M Surdeanu Proceedings of the 58th annual meeting of the Association for Computational …, 2020 | 16 | 2020 |
| It takes two flints to make a fire: Multitask learning of neural relation and explanation classifiers Z Tang, M Surdeanu Computational Linguistics 49 (1), 117-156, 2023 | 9 | 2023 |
| Pag-llm: Paraphrase and aggregate with large language models for minimizing intent classification errors V Yadav, Z Tang, V Srinivasan Proceedings of the 47th international ACM SIGIR conference on research and …, 2024 | 8* | 2024 |
| Interpretability rules: Jointly bootstrapping a neural relation extractorwith an explanation decoder Z Tang, M Surdeanu Proceedings of the first workshop on trustworthy natural language processing …, 2021 | 4 | 2021 |
| Disp-llm: Dimension-independent structural pruning for large language models S Gao, CH Lin, T Hua, Z Tang, Y Shen, H Jin, YC Hsu Advances in Neural Information Processing Systems 37, 72219-72244, 2024 | 3 | 2024 |
| Bootstrapping neural relation and explanation classifiers Z Tang, M Surdeanu Proceedings of the 61st Annual Meeting of the Association for Computational …, 2023 | 2 | 2023 |
| Dynamic Noise Preference Optimization for LLM Self-Improvement via Synthetic Data H Yang, T Hua, S Gao, B Xu, Z Tang, J Xu, H Jin, V Srinivasan arXiv preprint arXiv:2502.05400, 2025 | | 2025 |
| PARAPHRASE AND AGGREGATE WITH LARGE LANGUAGE MODELS FOR IMPROVED DECISIONS V Yadav, Z Tang, V Srinivasan, H Jin US Patent App. 18/781,617, 2025 | | 2025 |
| ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning S Gao, T Hua, R Shirkavand, CH Lin, Z Tang, Z Li, L Yuan, F Li, Z Zhang, ... arXiv preprint arXiv:2501.15316, 2025 | | 2025 |
| Interpretable Natural Language Processing with Applications to Information Extraction Z Tang The University of Arizona, 2022 | | 2022 |
| Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction J Hungerford, YS Chan, J MacBride, BM Gyori, A Zupon, Z Tang, ... Association for Computational Linguistics (ACL), 2022 | | 2022 |
| Alert Generation in Execution Monitoring Using Resource Envelopes. TKS Kumar, H Xu, Z Tang, A Kumar, CM Rogers, CA Knoblock FLAIRS, 38-43, 2018 | | 2018 |
| A Distributed Logical Filter for Connected Row Convex Constraints TKS Kumar, H Xu, Z Tang, A Kumar, CM Rogers, CA Knoblock 2017 IEEE 29th International Conference on Tools with Artificial …, 2017 | | 2017 |
Mihai SurdeanuProfessor, Computer Science, University of ArizonaZweryfikowany adres z email.arizona.edu