无需人工提示，自动优化大模型纠错与简化文本效果
APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification
- 不依赖人工种子提示，自动生成并优化提示词
- 在语法纠错和文本简化任务上达到纯大模型提示的最新性能
- 适合希望提升大模型零样本能力的研究者与开发者
大语言模型（LLMs）的发展使得自然语言处理任务可通过简单的提示交互完成。现有方法通过工程化提示（如思维链提示）提升模型表现。当有明确评估指标时，自动提示优化（APO）方法可改进初始提示。本文提出APIO，一种无需人工指定种子提示的自动提示诱导与优化方法，适用于语法纠错（GEC）和文本简化任务。该方法在纯大模型提示范式下实现了新最佳性能。相关数据、代码、提示及输出已公开。
原文摘要 · Abstract (English)
Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks to be performed through simple prompt-based interactions. Consequently, several approaches have been proposed to engineer prompts that most effectively enable LLMs to perform a given task (e.g., chain-of-thought prompting). In settings with a well-defined metric to optimize model performance, automatic prompt optimization (APO) methods have been developed to refine a seed prompt. Advancing this line of research, we propose APIO, a simple but effective prompt induction and optimization approach for the tasks of Grammatical Error Correction (GEC) and Text Simplification, without relying on manually specified seed prompts. APIO achieves a new state-of-the-art performance for purely LLM-based prompting methods on these tasks. We make our data, code, prompts, and outputs publicly available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。