Large Language Models as Optimizers (AI Summary)

Summary

The article introduces Optimization by PROmpting (OPRO), a novel approach that utilizes large language models (LLMs) as optimizers. The authors propose using natural language descriptions to guide the optimization process, with the LLM generating new solutions based on previous solutions and their values. These new solutions are then evaluated and incorporated into the prompt for the next optimization step.

To demonstrate the effectiveness of OPRO, the authors apply it to various optimization tasks. They first test it on linear regression and the traveling salesman problem, showing that LLMs can produce high-quality solutions and sometimes outperform hand-designed heuristic algorithms. The authors then focus on prompt optimization, aiming to find instructions that maximize task accuracy. By leveraging different LLMs, they show that prompts optimized by OPRO outperform human-designed prompts on different benchmarks by a significant margin.

The article also delves into different aspects of OPRO, including meta-prompt design, the number of generated instructions per step, the starting point for optimization, and the diversity of instructions. The authors find that the design choices of the meta-prompt, such as the order of previous instructions, the presentation of instruction scores, the inclusion of exemplars, and the number of generated instructions per step, can all impact the optimization performance.

Overall, the article presents OPRO as a promising approach for optimization using large language models. The results highlight its effectiveness in finding high-quality solutions and improving task accuracy. The authors also suggest future research directions, such as reducing sensitivity to initialization and striking a better balance between exploitation and exploration.

In addition to discussing prompt optimization, the article explores the use of different scorers and optimizers to enhance the performance of LLMs on various tasks. The authors compare the accuracies of instructions generated through prompt optimization with baseline instructions like "Let's think step by step" and the empty string. The consistent outperformance of prompt optimization over the baselines demonstrates its effectiveness in optimizing prompts for LLMs.

Key Points

1. Optimization by PROmpting (OPRO) is a simple and effective approach that utilizes large language models (LLMs) as optimizers by describing the optimization task in natural language. This approach has been demonstrated to be effective in solving linear regression and traveling salesman problems, showing that LLMs can find high-quality solutions and sometimes outperform hand-designed heuristic algorithms.

2. Another application of OPRO is prompt optimization, where the objective is to find the prompt that maximizes task accuracy. It has been observed that LLMs are sensitive to the prompt format, and optimizing prompts can significantly enhance performance.

3. OPRO has been evaluated on various benchmarks, including GSM8K and Big-Bench Hard tasks. The prompts optimized by OPRO consistently outperform human-designed prompts by a significant margin.

4. The meta-prompt design of OPRO plays a crucial role in optimization performance. Factors such as the order of previous instructions, the inclusion of instruction scores, and the presence of exemplars all impact the quality of the generated instructions.

5. The number of generated instructions per step and the choice of the initial instruction also have an influence on optimization performance.

6. OPRO is a promising approach that leverages LLMs as optimizers and has the potential to enhance problem optimization in diverse domains.

7. Future research directions for OPRO include reducing sensitivity to initialization, better balancing exploration and exploitation, and incorporating natural language feedback to further refine the optimization process.

8. OPRO represents a step towards utilizing LLMs for optimization tasks and highlights the potential of natural language processing in solving optimization problems.

Reference: https://arxiv.org/abs/2309.03409