AutoHint：基于提示生成的自动提示优化
AutoHint: Automatic Prompt Optimization with Hint Generation
AI总结：
本文提出AutoHint框架，通过从标注数据的错误预测中自动生成并汇总提示来增强初始提示，从而优化大语言模型的提示工程，并在BIG-Bench Instruction Induction数据集上显著提升多任务准确率。
摘要：
本文提出了AutoHint，一个用于大语言模型（LLM）自动提示工程与优化的新型框架。虽然LLM在各种任务中已展现出实现高质量标注的卓越能力，但将这种能力应用于特定任务的关键在于开发高质量的提示。因此，我们提出一个框架，通过融入从输入-输出示例中推导出的增强指令来优化原始提示，从而继承上下文学习与零样本学习两者的优点。我们将这种增强称为提示（hint），并提出一个框架从标注数据中自动生成提示。更具体地说，从初始提示开始，我们的方法首先指示LLM针对错误预测中的选定样本推导新的提示，然后对每个样本的提示进行汇总，并将结果添加回初始提示，以形成新的、增强的指令。所提出的方法在BIG-Bench Instruction Induction数据集上针对零样本和少样本提示进行了评估，实验表明我们的方法能够显著提升多个任务的准确率。
英文摘要：
This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability in achieving high-quality annotation in various tasks, the key to applying this ability to specific tasks lies in developing high-quality prompts. Thus we propose a framework to inherit the merits of both in-context learning and zero-shot learning by incorporating enriched instructions derived from input-output demonstrations to optimize original prompt. We refer to the enrichment as the hint and propose a framework to automatically generate the hint from labeled data. More concretely, starting from an initial prompt, our method first instructs a LLM to deduce new hints for selected samples from incorrect predictions, and then summarizes from per-sample hints and adds the results back to the initial prompt to form a new, enriched instruction. The proposed method is evaluated on the BIG-Bench Instruction Induction dataset for both zero-shot and few-short prompts, where experiments demonstrate our method is able to significantly boost accuracy for multiple tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
