🧑‍🔬 Who cites Bingzhe Wu

119 citations of their works from 119 papers of our library (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · ← Bingzhe Wu

1. WizardLM: Empowering large pre-trained language models to follow complex instructionsInternational Conference on Learning Representations (ICLR)Can Xu, Qingfeng Sun, Kai Zheng, et al.2023 · 741 citations
2. HybridFlow: A Flexible and Efficient RLHF FrameworkarXiv:2409.19256Guangming Sheng, Chi Zhang, Zilingfeng Ye, et al.2024 · 624 citations
3. The Prompt Report: A Systematic Survey of Prompt Engineering TechniquesarXiv:2406.06608Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.2024 · 189 citations
4. A Survey on Efficient Inference for Large Language ModelsarXiv:2404.14294Zixuan Zhou, Xuefei Ning, Ke Hong, et al.2024 · 113 citations
5. EfficientQAT: Efficient Quantization-Aware Training for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mengzhao Chen, Wenqi Shao, Peng Xu, et al.2025 · 82 citations
6. OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AIConference on Neural Information Processing Systems (NeurIPS)Zhen Huang, Zengzhi Wang, Shijie Xia, et al.2024 · 56 citations
7. In-context Learning with Retrieved Demonstrations for Language Models: A SurveyarXiv:2401.11624Man Luo, Xin Xu, Yue Liu, et al.2024 · 54 citations
8. ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error DetectionFindings of the Association for Computational LinguisticsYibo Yan, Shen Wang, Jiahao Huo, et al.2026 · 23 citations
9. Automatic Combination of Sample Selection Strategies for Few-Shot LearningFindings of the Association for Computational LinguisticsBranislav Pecher, Ivan Srba, Maria Bielikova, et al.2026 · 15 citations
10. Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?Annual Meeting of the Association for Computational Linguistics (ACL)Jiayu Liu, Qing Zong, Weiqi Wang, et al.2025 · 8 citations
11. MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Tao Zhang, Ziqian Zeng, Hao Peng, et al.2026 · 5 citations
12. Rethinking Prompt Optimizers: From Prompt Merits to OptimizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Zixiao Zhu, Hanzhang Zhou, Zijian Feng, et al.2026 · 3 citations
13. Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMsFindings of the Association for Computational LinguisticsYiheng Yang, Yujie Wang, Chi Ma, et al.2026 · 3 citations
14. SSSD: Simply-Scalable Speculative DecodingAnnual Meeting of the Association for Computational Linguistics (ACL)Michele Marzollo, Jiawei Zhuang, Niklas Roemer, et al.2026 · 3 citations
15. Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Qisheng Su, Shiting Huang, Zhen Fang, et al.2026 · 2 citations
16. Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point FormatsAnnual Meeting of the Association for Computational Linguistics (ACL)Manyi Zhang, Ji-Fu Li, Zhongao Sun, et al.2026
17. Calibrating Inference Time Alignment with Sequence-level Risk AccumulationAnnual Meeting of the Association for Computational Linguistics (ACL)Shanwen Tan, Ziyang Dong, Wei Ju, et al.2026
18. COMPEL: Compensated Mixture-of-Experts Pruning with Expert-Layer distributionFindings of the Association for Computational LinguisticsSeohee Yoon, Yong Suk Choi2026
19. pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware TrainingFindings of the Association for Computational LinguisticsWenzheng Zhang, Bingzheng Liu2026

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