StraGo: Harnessing Strategic Guidance for Prompt Optimization

• URL: http://arxiv.org/abs/2410.08601v1
• Date: Fri, 11 Oct 2024 07:55:42 GMT
• Title: StraGo: Harnessing Strategic Guidance for Prompt Optimization
• Authors: Yurong Wu, Yan Gao, Bin Benjamin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, Linjun Yang,
• Abstract summary: StraGo is a novel approach designed to mitigate prompt drifting by leveraging insights from both successful and failed cases. It employs a how-to-do methodology, integrating in-context learning to formulate specific, actionable strategies. Experiments conducted across a range of tasks, including reasoning, natural language understanding, domain-specific knowledge, and industrial applications, demonstrate StraGo's superior performance.
• Score: 35.96577924228001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt effectiveness, they often lead to prompt drifting, where newly generated prompts can adversely impact previously successful cases while addressing failures. Furthermore, these methods tend to rely heavily on LLMs' intrinsic capabilities for prompt optimization tasks. In this paper, we introduce StraGo (Strategic-Guided Optimization), a novel approach designed to mitigate prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. StraGo employs a how-to-do methodology, integrating in-context learning to formulate specific, actionable strategies that provide detailed, step-by-step guidance for prompt optimization. Extensive experiments conducted across a range of tasks, including reasoning, natural language understanding, domain-specific knowledge, and industrial applications, demonstrate StraGo's superior performance. It establishes a new state-of-the-art in prompt optimization, showcasing its ability to deliver stable and effective prompt improvements.

Related papers

• A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models [14.483240353801074]
  This paper proposes an optimal learning framework for automated prompt engineering. It is designed to sequentially identify effective prompt features while efficiently allocating a limited evaluation budget. Our framework provides a solution to deploying automated prompt engineering in a wider range applications.
  arXiv  Detail & Related papers  (2025-01-07T03:51:10Z)
• GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers [52.17222304851524]
  We introduce GReaTer, a novel prompt optimization technique that directly incorporates gradient information over task-specific reasoning. By utilizing task loss gradients, GReaTer enables self-optimization of prompts for open-source, lightweight language models. GReaTer consistently outperforms previous state-of-the-art prompt optimization methods.
  arXiv  Detail & Related papers  (2024-12-12T20:59:43Z)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries. We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks. Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• Dual-Phase Accelerated Prompt Optimization [29.261886603989694]
  We propose a dual-phase approach which starts with generating high-quality initial prompts. We iteratively optimize the prompts at the sentence level, leveraging previous tuning experience to expand prompt candidates and accept effective ones. Experiments on eight datasets demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2024-06-19T11:08:56Z)
• PromptWizard: Task-Aware Prompt Optimization Framework [2.618253052454435]
  Large language models (LLMs) have transformed AI across diverse domains. Manual prompt engineering is both labor-intensive and domain-specific. We introduce PromptWizard, a novel, fully automated framework for discrete prompt optimization.
  arXiv  Detail & Related papers  (2024-05-28T17:08:31Z)
• Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases [2.6159111710501506]
  We introduce a new method for automatic prompt engineering, using a calibration process that iteratively refines the prompt to the user intent. We demonstrate the effectiveness of our method with respect to strong proprietary models on real-world tasks such as moderation and generation.
  arXiv  Detail & Related papers  (2024-02-05T15:28:43Z)
• Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL [62.824464372594576]
  We aim to enhance arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization. We identify a previously overlooked objective of query dependency in such optimization. We introduce Prompt-OIRL, which harnesses offline inverse reinforcement learning to draw insights from offline prompting demonstration data.
  arXiv  Detail & Related papers  (2023-09-13T01:12:52Z)
• 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)
• RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning [84.75064077323098]
  This paper proposes RLPrompt, an efficient discrete prompt optimization approach with reinforcement learning (RL) RLPrompt is flexibly applicable to different types of LMs, such as masked gibberish (e.g., grammaBERT) and left-to-right models (e.g., GPTs) Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing finetuning or prompting methods.
  arXiv  Detail & Related papers  (2022-05-25T07:50:31Z)

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.