WorkDifferentWithAI.com Academic Paper Alert!
Written by Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen
Published by Google Deep Mind
Category: AI News
Article Section: Machine learning techniques
Publication Date: 2023-09-06
SEO Description: “Optimizing performance with Large Language Models in machine learning and AI applications.”
Claude Generated Paper Summary
- The paper proposes Optimization by Prompting (OPRO), which uses large language models (LLMs) as optimizers to solve problems by iteratively generating solutions based on past attempts described in natural language prompts.
- It demonstrates OPRO on small linear regression and traveling salesman problems, showing LLMs can optimize effectively just through prompting without formal problem specifications. GPT-4 notably outperforms GPT-3.5 and text-bison.
- For prompt optimization, the goal is to generate an instructional prompt that maximizes a task’s accuracy when applied to a scorer LLM. The prompt and accuracies of past attempts are provided as context.
- OPRO is evaluated on prompt optimization for GSM8K and BigBench. Various LLMs are shown to be effective optimizers, iteratively generating better prompts over optimization steps.
- The optimized prompts outperform human-designed prompts, delivering absolute accuracy gains of up to 8% on GSM8K and over 50% on some BigBench tasks.
- Different LLMs produce prompts of different styles. Transferability of optimized prompts across datasets of the same domain is demonstrated.
- Ablation studies analyze impact of different meta-prompt design choices and optimizer model hyperparameters.
In summary, the key ideas are using LLMs for optimization through iterative natural language prompting, showing effectiveness on prompt optimization, and analyzing design considerations. The results demonstrate LLMs’ promise for optimization.
I have a saying now that “we have entered the age of the meta prompt.” This paper seems to agree with that. What do you think?
I agree that the idea of using “meta prompts” to guide large language models is becoming increasingly popular and important. Some key thoughts on this:
- The paper provides a good example of how meta prompts can be designed to iteratively optimize prompts for a given task using LLMs. The meta prompt incorporates the prompt optimization trajectory and task descriptions.
- Using meta prompts is a natural fit for leveraging the few-shot learning capabilities of LLMs, where the meta prompt provides the contextual examples and instructions.
- Meta prompting does seem to be a defining technique of this current era of LLMs. By encoding instructions, examples, and context into meta prompts, users can customize LLMs more easily for different downstream tasks.
- There is growing research interest in meta prompt design, optimization, and learning prompts automatically. So meta prompting will likely grow as an area.
- But meta prompting alone has limitations. It may be brittle, prompt overfitting is a risk, and diversity of generated solutions can suffer. So complementary techniques will be needed.
- Overall, I agree we have entered an “age of the meta prompt” with the rise of LLMs. Meta prompting is a powerful, defining technique today. But research into more robust and automated meta prompting will be important going forward.
Keywords
Large Language Models, Optimizers, Optimization by PROmpting, machine learning, prompt optimization
Author’s Abstract
Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.