Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers

• URL: http://arxiv.org/abs/2402.17564v2
• Date: Wed, 17 Apr 2024 03:17:58 GMT
• Title: Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers
• Authors: Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao, Siyuan Lu, Yaliang Li, Ji-Rong Wen,
• Abstract summary: We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts. We identify two pivotal factors in model parameter learning: update direction and update method. In particular, we borrow the theoretical framework and learning methods from gradient-based optimization to design improved strategies.
• Score: 108.72225067368592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic prompt optimization is an important approach to improving the performance of large language models (LLMs). Recent research demonstrates the potential of using LLMs as prompt optimizers, which can generate improved task prompts via iterative refinement. In this paper, we propose a novel perspective to investigate the design of LLM-based prompt optimizers, by drawing an analogy with gradient-based model optimizers. To connect these two approaches, we identify two pivotal factors in model parameter learning: update direction and update method. Focused on the two aspects, we borrow the theoretical framework and learning methods from gradient-based optimization to design improved strategies for LLM-based prompt optimizers. By systematically analyzing a rich set of improvement strategies, we further develop a capable Gradient-inspired LLM-based Prompt Optimizer called GPO. At each step, it first retrieves relevant prompts from the optimization trajectory as the update direction. Then, it utilizes the generation-based refinement strategy to perform the update, while controlling the edit distance through a cosine-based decay strategy. Extensive experiments demonstrate the effectiveness and efficiency of GPO. In particular, GPO brings an additional improvement of up to 56.8% on Big-Bench Hard and 55.3% on MMLU compared to baseline methods.

Related papers

• Iterative or Innovative? A Problem-Oriented Perspective for Code Optimization [81.88668100203913]
  Large language models (LLMs) have demonstrated strong capabilities in solving a wide range of programming tasks. In this paper, we explore code optimization with a focus on performance enhancement, specifically aiming to optimize code for minimal execution time.
  arXiv  Detail & Related papers  (2024-06-17T16:10:10Z)
• Two Optimizers Are Better Than One: LLM Catalyst Empowers Gradient-Based Optimization for Prompt Tuning [69.95292905263393]
  We show that gradient-based optimization and large language models (MsLL) are complementary to each other, suggesting a collaborative optimization approach. Our code is released at https://www.guozix.com/guozix/LLM-catalyst.
  arXiv  Detail & Related papers  (2024-05-30T06:24:14Z)
• The Importance of Directional Feedback for LLM-based Optimizers [23.669705029245645]
  We study the potential of using large language models (LLMs) as an interactive for solving problems in a text space using natural language and numerical feedback. We design a new LLM-based that synthesizes directional feedback from the historical optimization trace to achieve reliable improvement over iterations.
  arXiv  Detail & Related papers  (2024-05-26T05:22:35Z)
• Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism [1.1463861912335864]
  Large Language Models (LLMs) have demonstrated remarkable reasoning abilities. This paper asserts that LLMs possess the capability for zero-shot optimization across diverse scenarios. We introduce a novel population-based method for numerical optimization using LLMs.
  arXiv  Detail & Related papers  (2024-03-04T13:57:37Z)
• Large Language Models As Evolution Strategies [6.873777465945062]
  In this work, we investigate whether large language models (LLMs) are in principle capable of implementing evolutionary optimization algorithms. We introduce a novel prompting strategy, consisting of least-to-most sorting of discretized population members. We find that our setup allows the user to obtain an LLM-based evolution strategy, which we call EvoLLM', that robustly outperforms baseline algorithms.
  arXiv  Detail & Related papers  (2024-02-28T15:02:17Z)
• FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema [36.65009632307124]
  We propose Free-from Instruction-oriented Prompt Optimization (FIPO) to improve task performance of large language models (LLMs) FIPO uses a modular APO template that dynamically integrate the naive task instruction, optional instruction responses, and optional ground truth to produce finely optimized prompts. We validate FIPO framework across five public benchmarks and three testing models.
  arXiv  Detail & Related papers  (2024-02-19T03:56:44Z)
• Are Large Language Models Good Prompt Optimizers? [65.48910201816223]
  We conduct a study to uncover the actual mechanism of LLM-based Prompt Optimization. Our findings reveal that the LLMs struggle to identify the true causes of errors during reflection, tending to be biased by their own prior knowledge. We introduce a new "Automatic Behavior Optimization" paradigm, which directly optimize the target model's behavior in a more controllable manner.
  arXiv  Detail & Related papers  (2024-02-03T09:48:54Z)
• Large Language Models as Optimizers [106.52386531624532]
  We propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as prompts. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values. 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.
  arXiv  Detail & Related papers  (2023-09-07T00:07:15Z)
• Learning to Optimize: A Primer and A Benchmark [94.29436694770953]
  Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods. This article is poised to be the first comprehensive survey and benchmark of L2O for continuous optimization.
  arXiv  Detail & Related papers  (2021-03-23T20:46:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.