Auto-Instruct：面向黑盒语言模型的自动指令生成与排序
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
AI总结：
本文提出Auto-Instruct方法，利用LLM生成候选指令并用575个NLP任务训练的评分模型排序，在118个域外任务上超越人工和基线指令，且具泛化能力。
摘要：
大型语言模型（LLMs）可以通过遵循自然语言指令来执行广泛的任务，而无需进行特定任务的微调。不幸的是，LLMs的性能极大地受这些指令质量的影响，并且为每个任务手动编写有效的指令是一个费时且主观的过程。在本文中，我们引入了Auto-Instruct，这是一种自动提高提供给LLMs指令质量的新方法。我们的方法利用LLMs固有的生成能力，为给定任务生成多样化的候选指令，然后使用在575个现有NLP任务上训练的评分模型对它们进行排序。在118个域外任务的实验中，Auto-Instruct超越了人工编写的指令和现有的LLM生成指令基线。此外，即使对于未纳入其训练过程的其他LLMs，我们的方法也展现出显著的泛化能力。
英文摘要：
Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning. Unfortunately, the performance of LLMs is greatly influenced by the quality of these instructions, and manually writing effective instructions for each task is a laborious and subjective process. In this paper, we introduce Auto-Instruct, a novel method to automatically improve the quality of instructions provided to LLMs. Our method leverages the inherent generative ability of LLMs to produce diverse candidate instructions for a given task, and then ranks them using a scoring model trained on a variety of 575 existing NLP tasks. In experiments on 118 out-of-domain tasks, Auto-Instruct surpasses both human-written instructions and existing baselines of LLM-generated instructions. Furthermore, our method exhibits notable generalizability even with other LLMs that are not incorporated into its training process.
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。
