LLM-Optimizers-Papers

Must-read Papers on Large Language Model (LLM) as Optimizers and Automatic Optimization for Prompting LLMs.

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🏷️ News

• 2023-10-06 We create this paper list on LLM as Optimizers and Automatic Optimization for Prompting LLMs .

🗞️ Papers

1. RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

   Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, Zhiting Hu. [abs]. EMNLP 2022.

2. Large Language Models Are Human-Level Prompt Engineers

   Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, Jimmy Ba. [abs]. ICLR 2023.

3. GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models

   Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal. [abs]. EACL 2023.

4. Automatic Prompt Optimization with "Gradient Descent" and Beam Search

   Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng. [abs]. Preprint 2023.05.

5. InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models

   Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou. [abs]. Preprint 2023.06.

6. Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization

   Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh Murthy, Zeyuan Chen, Jianguo Zhang, Devansh Arpit, Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese. [abs]. Preprint 2023.08.

7. Forget Demonstrations, Focus on Learning from Textual Instructions

   Renze Lou, Wenpeng Yin. [abs]. Preprint 2023.08.

8. Large Language Models as Optimizers

   Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen. [abs]. Preprint 2023.09.

9. Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

   Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel. [abs]. Preprint 2023.09.

10. Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers

    Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang. [abs]. Preprint 2023.09.

11. Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation

    Eric Zelikman, Eliana Lorch, Lester Mackey, Adam Tauman Kalai. [abs]. Preprint 2023.10.

12. DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

    Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, Christopher Potts. [abs]. Preprint 2023.10.

13. Eureka: Human-Level Reward Design via Coding Large Language Models

    Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, Anima Anandkumar. [abs]. Preprint 2023.10.

14. PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization

    Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, Zhiting Hu. [abs], Preprint 2023.10

15. OptiMUS: Optimization Modeling Using MIP Solvers and large language models

    Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell. [abs], Preprint 2023.10

16. AI-Copilot for Business Optimisation: A Framework and A Case Study in Production Scheduling

    Pivithuru Thejan Amarasinghe, Su Nguyen, Yuan Sun, Damminda Alahakoon. [abs], Preprint 2023.10

17. Enhancing Genetic Improvement Mutations Using Large Language Models

    Alexander E.I. Brownlee, James Callan, Karine Even-Mendoza, Alina Geiger, Carol Hanna, Justyna Petke, Federica Sarro, Dominik Sobania. [abs], Preprint 2023.10

18. Language Model Decoding as Direct Metrics Optimization

    Haozhe Ji, Pei Ke, Hongning Wang, Minlie Huang. [abs], Preprint 2023.10

19. Large Language Models to Enhance Bayesian Optimization

    ICLR 2024 Conference Submission8133 Authors. [openreview], ICLR submission 2023.10

20. Large Language Models as Evolutionary Optimizers

    Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, Yew-Soon Ong. [abs], Preprint 2023.11

21. Large Language Models can Implement Policy Iteration

    Ethan Brooks, Logan Walls, Richard L. Lewis, Satinder Singh. [abs], NeurIPS 2023

22. Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

    Noah Hollmann, Samuel Müller, Frank Hutter. [abs], NeurIPS 2023

23. Plum: Prompt Learning using Metaheuristic

    Rui Pan, Shuo Xing, Shizhe Diao, Xiang Liu, Kashun Shum, Jipeng Zhang, Tong Zhang. [abs], Preprint 2023.11

24. ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution

    Haoran Ye, Jiarui Wang, Zhiguang Cao, Guojie Song. [abs], [code], Preprint 2024.02

25. Training Language Model Agents without Modifying Language Models

    Shaokun Zhang, Jieyu Zhang, Jiale Liu, Linxin Song, Chi Wang, Ranjay Krishna, Qingyun Wu [abs], Preprint 2024.02

26. LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation

    Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Guojun Peng, Zhiguang Cao, Yining Ma, Yue-Jiao Gong, [abs], Preprint 2024.03

27. Language Agents as Optimizable Graphs

    Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber, [abs], [code], [page], Preprint 2024.03

📚 Resources

1. Large Language Models / Foundation Models for Combinatorial Optimization: https://github.com/ai4co/awesome-fm4co

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