🔗 Who cites «Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases»

43 papers of our library cite it (of the 141,840 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases»

1. Qwen Technical ReportarXiv:2309.16609Jinze Bai, Shuai Bai, Yunfei Chu, et al.2023 · 1,645 citations
2. Yi: Open Foundation Models by 01.AIarXiv:2403.04652Alex Young, Bei Chen, Chao Li, et al.2024 · 402 citations
3. Enhancing Chat Language Models by Scaling High-quality Instructional ConversationsarXiv:2305.14233Ning Ding, Yulin Chen, Bokai Xu, et al.2023 · 383 citations
4. PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning OptimizationarXiv:2306.05087Yidong Wang, Zhuohao Yu, Zhengran Zeng, et al.2023 · 192 citations
5. LawBench: Benchmarking Legal Knowledge of Large Language ModelsarXiv:2309.16289Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.2023 · 186 citations
6. RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language ModelsarXiv:2310.00746Zekun Moore Wang, Zhongyuan Peng, Haoran Que, et al.2023 · 186 citations
7. Phoenix: Democratizing ChatGPT across LanguagesarXiv:2304.10453Zhihong Chen, Feng Jiang, Junying Chen, et al.2023 · 68 citations
   One-Shot Learning as Instruction Data Prospector for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yunshui Li, Binyuan Hui, Xiaobo Xia, et al.2024 · 61 citations

35 more papers of our library cite it; they have no page here yet.

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