| WizardLM: Empowering Large Language Models to Follow Complex Instructions C Xu*, Q Sun*, K Zheng*, X Geng, P Zhao, J Feng, C Tao, D Jiang Proc. ICLR, 2023 | 838* | 2023 |
| WizardCoder: Empowering Code Large Language Models with Evol-Instruct Z Luo, C Xu, P Zhao, Q Sun, X Geng, W Hu, C Tao, J Ma, Q Lin, D Jiang Proc. ICLR, 2023 | 550 | 2023 |
| WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct H Luo, Q Sun, C Xu, P Zhao, J Lou, C Tao, X Geng, Q Lin, S Chen, ... Proc. ICLR, 2023 | 331 | 2023 |
| RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems C Tao, L Mou, D Zhao, R Yan Proc. AAAI, 722--729, 2018 | 260 | 2018 |
| Knowledge-Grounded Dialogue Generation with Pre-trained Language Models X Zhao, W Wu, C Xu, C Tao, D Zhao, R Yan Proc. EMNLP, 2020 | 226 | 2020 |
| Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation W Hu*, Z Lin*, B Liu*, C Tao, Z Tao, J Ma, D Zhao, R Yan Proc. ICLR, 2018 | 190 | 2018 |
| Get The Point of My Utterance! Learning Towards Effective Responses with Multi-Head Attention Mechanism. C Tao, S Gao, M Shang, W Wu, D Zhao, R Yan Proc. IJCAI, 4418-4424, 2018 | 162 | 2018 |
| Multi-Representation Fusion Network for Multi-Turn Response Selection in Retrieval-Based Chatbots C Tao, W Wu, C Xu, W Hu, D Zhao, R Yan Proc. WSDM, 267-275, 2019 | 156 | 2019 |
| One Time of Interaction May Not be Enough: Go Deep With an Interaction-over-interaction Network for Response Selection in Dialogues C Tao, W Wu, C Xu, W Hu, D Zhao, R Yan Proc. ACL, 2019 | 139 | 2019 |
| A survey on knowledge distillation of large language models X Xu, M Li, C Tao#, T Shen, R Cheng, J Li, C Xu, D Tao, T Zhou arXiv preprint arXiv:2402.13116, 2024 | 119 | 2024 |
| Low-Resource Knowledge-Grounded Dialogue Generation X Zhao, W Wu, C Tao, C Xu, D Zhao, R Yan Proc. ICLR, 2020 | 117 | 2020 |
| PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks Y Wang, C Xu, Q Sun, H Hu, C Tao, X Geng, D Jiang Proc. ACL, 2022 | 88 | 2022 |
| Zero-Resource Knowledge-Grounded Dialogue Generation L Li, C Xu, W Wu, Y Zhao, X Zhao, C Tao Proc. NeurIPS, 2020 | 80 | 2020 |
| Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues R Xu, C Tao, D Jiang, X Zhao, D Zhao, R Yan Proc. AAAI, 2021 | 69 | 2021 |
| Multi-Granularity Structural Knowledge Distillation for Language Model Compression C Liu, C Tao, J Feng, D Zhao Proc. ACL, 1001-1011, 2022 | 55 | 2022 |
| Leveraging Large Language Models for NLG Evaluation: Advances and Challenges Z Li*, X Xu*, T Shen, C Xu, JC Gu, Y Lai, C Tao#, S Ma Proc. EMNLP, 2024 | 54* | 2024 |
| MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding JC Gu, C Tao, ZH Ling, C Xu, X Geng, D Jiang Proc. ACL, 2021 | 53 | 2021 |
| Towards Robust Ranker for Text Retrieval Y Zhou, T Shen, X Geng, C Tao, C Xu, G Long, B Jiao, D Jiang Proc. ACL Findings, 2022 | 48 | 2022 |
| Iterative Document Representation Learning Towards Summarization with Polishing X Chen, S Gao, C Tao, Y Song, D Zhao, R Yan Proc. EMNLP, 2018 | 47 | 2018 |
| Thread of thought unraveling chaotic contexts Y Zhou, X Geng, T Shen, C Tao, G Long, JG Lou, J Shen arXiv preprint arXiv:2311.08734, 2023 | 46 | 2023 |
Can XuMicrosoft AI確認したメール アドレス: microsoft.com