A comprehensive capability analysis of gpt-3 and gpt-3.5 series models J Ye, X Chen, N Xu, C Zu, Z Shao, S Liu, Y Cui, Z Zhou, C Gong, Y Shen, ... arXiv preprint arXiv:2303.10420, 2023 | 356* | 2023 |
Secrets of rlhf in large language models part i: Ppo R Zheng, S Dou, S Gao, Y Hua, W Shen, B Wang, Y Liu, S Jin, Q Liu, ... arXiv preprint arXiv:2307.04964, 2023 | 112 | 2023 |
How robust is gpt-3.5 to predecessors? a comprehensive study on language understanding tasks X Chen, J Ye, C Zu, N Xu, R Zheng, M Peng, J Zhou, T Gui, Q Zhang, ... arXiv preprint arXiv:2303.00293, 2023 | 80* | 2023 |
Secrets of rlhf in large language models part ii: Reward modeling B Wang, R Zheng, L Chen, Y Liu, S Dou, C Huang, W Shen, S Jin, E Zhou, ... arXiv preprint arXiv:2401.06080, 2024 | 70 | 2024 |
Llm-da: Data augmentation via large language models for few-shot named entity recognition J Ye, N Xu, Y Wang, J Zhou, Q Zhang, T Gui, X Huang arXiv preprint arXiv:2402.14568, 2024 | 36 | 2024 |
An exploration of prompt-based zero-shot relation extraction method J Zhao, Y Hu, N Xu, T Gui, Q Zhang, Y Chen, X Gao China National Conference on Chinese Computational Linguistics, 81-95, 2022 | 7 | 2022 |
How robust is GPT-3.5 to predecessors X Chen, J Ye, C Zu, N Xu, R Zheng, M Peng, J Zhou, T Gui, Q Zhang, ... A comprehensive study on language understanding tasks. arXiv e-prints, arXiv …, 2023 | 6 | 2023 |
Delve into PPO: Implementation matters for stable RLHF R Zheng, S Dou, S Gao, Y Hua, W Shen, B Wang, Y Liu, S Jin, Y Zhou, ... NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following, 2023 | 6 | 2023 |
Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution N Xu, J Zhao, C Zu, W Qin, T Gui, Q Zhang, X Huang arXiv:2402.11525v3, 2024 | 1 | 2024 |
Abstains from Prediction: Towards Robust Relation Extraction in Real World J Zhao, Y Zhang, N Xu, T Gui, Q Zhang, Y Chen, X Gao China National Conference on Chinese Computational Linguistics, 96-111, 2022 | | 2022 |