wasiahmad/Awesome-LLM-Synthetic-Data

A reading list on LLM based Synthetic Data Generation 🔥

Synthetic Data of LLMs, by LLMs, for LLMs

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

Thanks for all the great contributors on GitHub!🔥⚡🔥

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

• 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.
• Self-instruct: Aligning language models with self-generated instructions Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi. ACL 2023.
• Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling Pratyush Maini, Skyler Seto, He Bai, David Grangier, Yizhe Zhang, Navdeep Jaitly. ACL 2024.
• TarGEN: Targeted Data Generation with Large Language Models Himanshu Gupta, Kevin Scaria, Ujjwala Anantheswaran, Shreyas Verma, Mihir Parmar, Saurabh Arjun Sawant, Chitta Baral, Swaroop Mishra. COLM 2024.
• Automatic Instruction Evolving for Large Language Models Weihao Zeng, Can Xu, Yingxiu Zhao, Jian-Guang Lou, Weizhu Chen. Arxiv 2024.
• Scaling Synthetic Data Creation with 1,000,000,000 Personas Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, Dong Yu. Arxiv 2024.
• Self-playing Adversarial Language Game Enhances LLM Reasoning Pengyu Cheng, Tianhao Hu, Han Xu, Zhisong Zhang, Yong Dai, Lei Han, Nan Du Arxiv 2024.
• Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources Alisia Lupidi, Carlos Gemmell, Nicola Cancedda, Jane Dwivedi-Yu, Jason Weston, Jakob Foerster, Roberta Raileanu, Maria Lomeli Arxiv 2024.
• Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach ACL Findings 2024.
• Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing Zhangchen Xu, Fengqing Jiang, Luyao Niu, Yuntian Deng, Radha Poovendran, Yejin Choi, Bill Yuchen Lin Arxiv 2024.

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

• 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 Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, Lingming Zhang. ICML 2024.
• InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct Yutong Wu, Di Huang, Wenxuan Shi, Wei Wang, Lingzhe Gao, Shihao Liu, Ziyuan Nan, Kaizhao Yuan, Rui Zhang, Xishan Zhang, Zidong Du, Qi Guo, Yewen Pu, Dawei Yin, Xing Hu, Yunji Chen. Arxiv 2024.
• OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, Wenhu Chen, Xiang Yue. Arxiv 2024.
• AutoCoder: Enhancing Code Large Language Model with AIEV-Instruct Bin Lei, Yuchen Li, Qiuwu Chen. Arxiv 2024.
• How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data Yejie Wang, Keqing He, Dayuan Fu, Zhuoma Gongque, Heyang Xu, Yanxu Chen, Zhexu Wang, Yujia Fu, Guanting Dong, Muxi Diao, Jingang Wang, Mengdi Zhang, Xunliang Cai, Weiran Xu. Arxiv 2024.
• SelfCodeAlign: Self-Alignment for Code Generation Yuxiang Wei, Federico Cassano, Jiawei Liu, Yifeng Ding, Naman Jain, Zachary Mueller, Harm de Vries, Leandro von Werra, Arjun Guha, Lingming Zhang. Arxiv 2024.

3.3. Text-to-SQL

• Synthesizing Text-to-SQL Data from Weak and Strong LLMs Jiaxi Yang, Binyuan Hui, Min Yang, Jian Yang, Junyang Lin, Chang Zhou. ACL 2024.

3.4. Alignment

• Constitutional AI: Harmlessness from AI Feedback Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, Jared Kaplan. Arxiv 2022.
• Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, Chuang Gan. NeurIPS 2023.
• SALMON: Self-Alignment with Instructable Reward Models Zhiqing Sun, Yikang Shen, Hongxin Zhang, Qinhong Zhou, Zhenfang Chen, David Cox, Yiming Yang, Chuang Gan. ICLR 2024.
• Refined Direct Preference Optimization with Synthetic Data for Behavioral Alignment of LLMs V´ıctor Gallego. Arxiv 2024.
• Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models Guanting Dong, Keming Lu, Chengpeng Li, Tingyu Xia, Bowen Yu, Chang Zhou, Jingren Zhou Arxiv 2024.
• Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, Tim Rocktäschel, Roberta Raileanu. NeurIPS 2024.

3.5. Reward Modeling

• West-of-N: Synthetic Preference Generation for Improved Reward Modeling Alizée Pace, Jonathan Mallinson, Eric Malmi, Sebastian Krause, Aliaksei Severyn. Arxiv 2024.

3.6. Long Context

• Make Your LLM Fully Utilize the Context. Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou. Arxiv 2024.
• From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data Zheyang Xiong, Vasilis Papageorgiou, Kangwook Lee, Dimitris Papailiopoulos. Arxiv 2024.

3.7. Weak-to-Strong

• Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Models Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, Yejin Choi. NAACL 2024.

3.8. Agent and Tool Use

• Toolformer: Language Models Can Teach Themselves to Use Tools Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom. NeurIPS 2023.
• GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction Rui Yang, Lin Song, Yanwei Li, Sijie Zhao, Yixiao Ge, Xiu Li, Ying Shan. Arxiv 2024.
• Gorilla: Large Language Model Connected with Massive APIs Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez. Arxiv 2023.
• ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun. Arxiv 2023.
• Voyager: An Open-Ended Embodied Agent with Large Language Models Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar. Arxiv 2023.

3.9. Vision and Language

• Visual Instruction Tuning Haotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae Lee. NeurIPS 2023.
• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny. ICLR 2024.
• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, Jingren Zhou. Arxiv 2023.
• G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, Lingpeng Kong. Arxiv 2023.
• Enhancing Large Vision Language Models with Self-Training on Image Comprehension Yihe Deng, Pan Lu, Fan Yin, Ziniu Hu, Sheng Shen, James Zou, Kai-Wei Chang, Wei Wang. Arxiv 2024.
• LLaVA-OneVision: Easy Visual Task Transfer Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li Arxiv 2024.

3.10. Factuality

• Fine-tuning Language Models for Factuality Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher D. Manning, Chelsea Finn. Arxiv 2023.
• MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents Liyan Tang, Philippe Laban, Greg Durrett. Arxiv 2024.

4. Datasets

• Synthetic-Text-To-SQL: A synthetic dataset for training language models to generate SQL queries from natural language prompts Meyer, Yev and Emadi, Marjan and Nathawani, Dhruv and Ramaswamy, Lipika and Boyd, Kendrick and Van Segbroeck, Maarten and Grossman, Matthew and Mlocek, Piotr and Newberry, Drew. Huggingface 2024.
• Open Artificial Knowledge Vadim Borisov, Richard Schreiber. ICML Workshop 2024.
• Code Alpaca: An Instruction-following LLaMA Model trained on code generation instructions Sahil Chaudhary. GitHub 2023.
• SynthPAI: A Synthetic Dataset for Personal Attribute Inference Hanna Yukhymenko, Robin Staab, Mark Vero, Martin Vechev. NeurIPS D&B 2024.

5. Tools

• DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows Ajay Patel, Colin Raffel, Chris Callison-Burch. ACL 2024.
• AgentInstruct: Toward Generative Teaching with Agentic Flows Arindam Mitra, Luciano Del Corro, Guoqing Zheng, Shweti Mahajan, Dany Rouhana, Andres Codas, Yadong Lu, Wei-ge Chen, Olga Vrousgos, Corby Rosset, Fillipe Silva, Hamed Khanpour, Yash Lara, Ahmed Awadallah. Arxiv 2024.
• Distilabel: An AI Feedback (AIF) Framework for Building Datasets with and for LLMs Álvaro Bartolomé Del Canto, Gabriel Martín Blázquez, Agustín Piqueres Lajarín and Daniel Vila Suero. GitHub 2024.
• Fuxion: Synthetic Data Generation and Normalization Functions using Langchain + LLMs

6. Blogs

• Synthetic dataset generation techniques: Self-Instruct Daniel van Strien. 2024
• LLM-Driven Synthetic Data Generation, Curation & Evaluation Cobus Greyling. 2024
• The Rise of Agentic Data Generation Maxime Labonne. 2024