| Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning DG DeepSeek-AI, D Yang, H Zhang, J Song, R Zhang, R Xu, Q Zhu, S Ma, ... arXiv preprint arXiv:2501.12948, 2025 | 365 | 2025 |
| Deepseekmath: Pushing the limits of mathematical reasoning in open language models Z Shao, P Wang, Q Zhu, R Xu, J Song, X Bi, H Zhang, M Zhang, YK Li, ... arXiv preprint arXiv:2402.03300, 2024 | 349* | 2024 |
| Double graph based reasoning for document-level relation extraction S Zeng, R Xu, B Chang, L Li arXiv preprint arXiv:2009.13752, 2020 | 271 | 2020 |
| Deepseek llm: Scaling open-source language models with longtermism X Bi, D Chen, G Chen, S Chen, D Dai, C Deng, H Ding, K Dong, Q Du, ... arXiv preprint arXiv:2401.02954, 2024 | 256 | 2024 |
| Math-shepherd: Verify and reinforce llms step-by-step without human annotations P Wang, L Li, Z Shao, RX Xu, D Dai, Y Li, D Chen, Y Wu, Z Sui arXiv preprint arXiv:2312.08935, 2023 | 232* | 2023 |
| Deepseek-v3 technical report A Liu, B Feng, B Xue, B Wang, B Wu, C Lu, C Zhao, C Deng, C Zhang, ... arXiv preprint arXiv:2412.19437, 2024 | 226 | 2024 |
| Deepseekmoe: Towards ultimate expert specialization in mixture-of-experts language models D Dai, C Deng, C Zhao, RX Xu, H Gao, D Chen, J Li, W Zeng, X Yu, Y Wu, ... arXiv preprint arXiv:2401.06066, 2024 | 190 | 2024 |
| Raise a child in large language model: Towards effective and generalizable fine-tuning R Xu, F Luo, Z Zhang, C Tan, B Chang, S Huang, F Huang arXiv preprint arXiv:2109.05687, 2021 | 190 | 2021 |
| DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model DS AI | 164 | 2024 |
| Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence Q Zhu, D Guo, Z Shao, D Yang, P Wang, R Xu, Y Wu, Y Li, H Gao, S Ma, ... arXiv preprint arXiv:2406.11931, 2024 | 147 | 2024 |
| Document-level event extraction via heterogeneous graph-based interaction model with a tracker R Xu, T Liu, L Li, B Chang arXiv preprint arXiv:2105.14924, 2021 | 120 | 2021 |
| Multimodal arxiv: A dataset for improving scientific comprehension of large vision-language models L Li, Y Wang, R Xu, P Wang, X Feng, L Kong, Q Liu arXiv preprint arXiv:2403.00231, 2024 | 83 | 2024 |
| A two-stream AMR-enhanced model for document-level event argument extraction R Xu, P Wang, T Liu, S Zeng, B Chang, Z Sui arXiv preprint arXiv:2205.00241, 2022 | 64 | 2022 |
| An enhanced span-based decomposition method for few-shot sequence labeling P Wang, R Xu, T Liu, Q Zhou, Y Cao, B Chang, Z Sui arXiv preprint arXiv:2109.13023, 2021 | 57 | 2021 |
| Omni-math: A universal olympiad level mathematic benchmark for large language models B Gao, F Song, Z Yang, Z Cai, Y Miao, Q Dong, L Li, C Ma, L Chen, R Xu, ... arXiv preprint arXiv:2410.07985, 2024 | 43* | 2024 |
| Making pre-trained language models end-to-end few-shot learners with contrastive prompt tuning Z Xu, C Wang, M Qiu, F Luo, R Xu, S Huang, J Huang Proceedings of the sixteenth ACM international conference on web search and …, 2023 | 31 | 2023 |
| From dense to sparse: Contrastive pruning for better pre-trained language model compression R Xu, F Luo, C Wang, B Chang, J Huang, S Huang, F Huang Proceedings of the AAAI Conference on Artificial Intelligence 36 (10), 11547 …, 2022 | 27 | 2022 |
| Behind the scenes: An exploration of trigger biases problem in few-shot event classification P Wang, R Xun, T Liu, D Dai, B Chang, Z Sui Proceedings of the 30th ACM International Conference on Information …, 2021 | 17 | 2021 |
| ATP: AMRize then parse! enhancing AMR parsing with PseudoAMRs L Chen, P Wang, R Xu, T Liu, Z Sui, B Chang arXiv preprint arXiv:2204.08875, 2022 | 16 | 2022 |
| Llm critics help catch bugs in mathematics: Towards a better mathematical verifier with natural language feedback B Gao, Z Cai, R Xu, P Wang, C Zheng, R Lin, K Lu, D Liu, C Zhou, W Xiao, ... arXiv preprint arXiv:2406.14024, 2024 | 13* | 2024 |
Baobao CHANGPeking UniversityVerified email at pku.edu.cn
