Synthetic Data of LLMs, by LLMs, for LLMs

This repo includes papers, tools, and blogs about Synthetic Data of LLMs, by LLMs, for LLMs.

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Contents

• Synthetic Data of LLMs, by LLMs, for LLMs
  • Contents
    • 1. Surveys
  • 2. Methods
    • 2.1. Techniques
    • 2.2. Instruction Generation with High Quality/Complexity
  • 3. Application Areas
    • 3.1. Mathematical Reasoning
    • 3.2. Code Generation
    • 3.3. Text-to-SQL
    • 3.4. Alignment
    • 3.5. Reward Modeling
    • 3.6. Long Context
    • 3.7. Weak-to-Strong
    • 3.8. Agent and Tool Use
    • 3.9. Vision and Language
    • 3.10. Factuality
  • 4. Datasets
  • 5. Tools
  • 6. Blogs

1. Surveys

• Recent Advances in Large Langauge Model Benchmarks against Data Contamination: From Static to Dynamic Evaluation Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray. Preprint'25
• A Survey on Bridging VLMs and Synthetic Data Mohammad Ghiasvand Mohammadkhani, Saeedeh Momtazi, Hamid Beigy. TechRxiv 2025.
• Best Practices and Lessons Learned on Synthetic Data for Language Models Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, Andrew M. Dai. COLM 2024.
• On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, Haobo Wang. Arxiv 2024.
• Large Language Models for Data Annotation: A Survey Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu. Arxiv 2024.
• Generative AI for Synthetic Data Generation: Methods, Challenges and the Future Xu Guo, Yiqiang Chen. Arxiv 2024.
• Comprehensive Exploration of Synthetic Data Generation: A Survey André Bauer, Simon Trapp, Michael Stenger, Robert Leppich, Samuel Kounev, Mark Leznik, Kyle Chard, Ian Foster. Arxiv 2024.

2. Methods

2.1. Techniques

• STaR: Bootstrapping Reasoning With Reasoning Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman. NeurIPS 2022.
• Symbolic Knowledge Distillation: from General Language Models to Commonsense Models Peter West, Chandra Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi. NAACL 2022.
• Generating Training Data with Language Models: Towards Zero-Shot Language Understanding Yu Meng, Jiaxin Huang, Yu Zhang, Jiawei Han. NeurIPS 2022.
• ZeroGen: Efficient Zero-shot Learning via Dataset Generation Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong. EMNLP 2022.
• Large Language Models Can Self-Improve Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han. EMNLP 2023.
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem. NeurIPS 2023.
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu. ICML 2024.
• Self-Rewarding Language Models. Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, Jason Weston. Arxiv 2024.
• Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models Haoran Li, Qingxiu Dong, Zhengyang Tang, Chaojun Wang, Xingxing Zhang, Haoyang Huang, Shaohan Huang, Xiaolong Huang, Zeqiang Huang, Dongdong Zhang, Yuxian Gu, Xin Cheng, Xun Wang, Si-Qing Chen, Li Dong, Wei Lu, Zhifang Sui, Benyou Wang, Wai Lam, Furu Wei. Arxiv 2024.
• ACL 2023.

2.2. Instruction Generation with High Quality/Complexity

• CodecLM: Aligning Language Models with Tailored Synthetic Data Zifeng Wang, Chun-Liang Li, Vincent Perot, Long T. Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister. Findings of NAACL 2024.
• WizardLM: Empowering Large Language Models to Follow Complex Instructions Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Daxin Jiang. Arxiv 2023.

3. Application Areas

3.1. Mathematical Reasoning

• MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, Chang Zhou. ACL 2024.
• MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs Zimu Lu, Aojun Zhou, Houxing Ren, Ke Wang, Weikang Shi, Junting Pan, Mingjie Zhan, Hongsheng Li. ACL 2024.
• MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu. ICLR 2024.
• Augmenting Math Word Problems via Iterative Question Composing Haoxiong Liu, Yifan Zhang, Yifan Luo, Andrew Chi-Chih Yao. DPFM@ICLR 2024.
• Distilling LLMs' Decomposition Abilities into Compact Language Models Denis Tarasov, Kumar Shridhar. AutoRL@ICML 2024.

3.2. Code Generation

• DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination Simin Chen, Pranav Pusarla, Baishakhi Ray. ICML 2025.
• PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models Simin Chen, Xiaoning Feng, XIaohong Han, Cong Liu, Wei Yang. FSE 2024
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven C.H. Hoi. NeurIPS 2022.
• Language Models Can Teach Themselves to Program Better Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai. ICLR 2023.
• InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback John Yang, Akshara Prabhakar, Karthik Narasimhan, Shunyu Yao. Arxiv 2023.
• Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models Somshubra Majumdar, Vahid Noroozi, Sean Narenthiran, Aleksander Ficek, Jagadeesh Balam, Boris Ginsburg. Arxiv 2024.
• Learning Performance-Improving Code Edits Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob Gardner, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh. ICLR 2024.
• WizardCoder: Empowering Code Large Language Models with Evol-Instruct Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, Daxin Jiang. ICLR 2024.
• WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, Qiufeng Yin. ACL 2024.
• [Magicoder: Empowering Code Generation with OSS-Instruct](https: