length-controlled win rate on AlpacaEval 2.0 (Dubois et al., 2024), 8.97 overall score on MT-Bench (Zheng et al., 2023), and 7.91 overall score on AlignBench (Liu et al., 2023). The English open-ended conversation datasets include Pile (Gao et al., 2020). Chinese understanding and culture datasets include CHID (Zheng et al., 2019) and CCPM (Li et al., 2021). Math datasets include GSM8K (Cobbe et al., 2021), MATH benchmarks, we further evaluate our model on open-ended conversation benchmarks including MT-Bench (Zheng et al., 2023), AlpacaEval 2.0 (Dubois et al., 2024), and AlignBench (Liu et al., 2023). For comparison

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upstream for related changes Special Thanks We’d like to thank the contributors to this work: Yinyong Zheng, Wei Zhang, Yong Zhang, Jun Chen, Yang Yang, Ai Jing, Huanxin Shi, Xiaoyun Mao, Ziteng Xu, Junxian

et al, 2010] Different Blur Assumptions Non-uniform: [Whyte et al, 2010], [Hirsch et al, 2011], [Zheng et al, 2013], etc. Previous Work 77 Data from [Whyte et al, 2010] Different Blur Assumptions Dynamic:

- Language Models are Few Shot learners. Available at: https://arxiv.org/pdf/2005.14165.pdf. 8. Zheng, L., et al., 2023, Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models.

化完善。 8. 作者简介 付浩、宪鹏、祥洲、玉基、徐灏、梦迪、武威等，均来自美团平台 / 搜索与 NLP 部。 9. 参考文献 [1] Cai, Hongyun, Vincent W. Zheng, and Kevin Chen-Chuan Chang. “A comprehensive survey of graph embedding: Problems, techniques, and Representations (2020). [16] https://github.com/dmlc/dgl/tree/master/dglgo [17] Wang, Minjie, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou et al. “Deep graph library: A graph-centric ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021: 3795-3805. [6] Chang J, Gao C, Zheng Y, et al. Sequential recommendation with graph neural networks[C]//Proceedings of the 44th International

of the IEEE Conference on Computer Vision and Pattern Recognition. 7746–7755. [7] Tianhao Yang, Zheng-Jun Zha, and Hanwang Zhang. 2019. Making History Matter: History-Advantage Sequence Training for for face detection in large scale variations[J]. arXiv preprint arXiv:1804.06559, 2018. [47] Zheng Z, Wang P, Liu W, et al. Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression[J]