---
title: "Who cites Exploring the Impact of Instruction — 43 citing papers"
description: "43 papers of the Elamur Research library cite «Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical…"
canonical: "https://research.elamur.ai/free/en/papers/ji-2023-exploring-impact-instruction-data/cited-by/"
---
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# 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»](https://md.research.elamur.ai/free/en/papers/ji-2023-exploring-impact-instruction-data/)

1. [**Qwen Technical Report arXiv:2309.16609**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,645 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
2. [**Yi: Open Foundation Models by 01.AI arXiv:2403.04652**Alex Young, Bei Chen, Chao Li, et al.*2024 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
3. [**Enhancing Chat Language Models by Scaling High-quality Instructional Conversations arXiv:2305.14233**Ning Ding, Yulin Chen, Bokai Xu, et al.*2023 · 383 citations*](https://md.research.elamur.ai/free/en/papers/ding-2023-enhancing-chat-language-models-scaling/)
4. [**PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization arXiv:2306.05087**Yidong Wang, Zhuohao Yu, Zhengran Zeng, et al.*2023 · 192 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-pandalm-automatic-evaluation-benchmark-llm/)
5. [**LawBench: Benchmarking Legal Knowledge of Large Language Models arXiv:2309.16289**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/)
6. [**RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models arXiv:2310.00746**Zekun Moore Wang, Zhongyuan Peng, Haoran Que, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-rolellm-benchmarking-eliciting-enhancing-role/)
7. [**Phoenix: Democratizing ChatGPT across Languages arXiv:2304.10453**Zhihong Chen, Feng Jiang, Junying Chen, et al.*2023 · 68 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-phoenix-democratizing-chatgpt-across-languages/)
8. **[One-Shot Learning as Instruction Data Prospector for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-one-shot-learning-as-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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.

- [← back to](https://md.research.elamur.ai/free/en/papers/ji-2023-exploring-impact-instruction-data/) — Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases