自动指导：针对黑盒语言模型的自动生成和排序指导

摘要

大型语言模型（LLMs）可以通过遵循自然语言指令执行各种任务，无需进行特定任务的微调。不幸的是，LLMs的性能在很大程度上受这些指令质量的影响，为每个任务手动编写有效指令是一个费时且主观的过程。在本文中，我们介绍了Auto-Instruct，一种新颖的方法，可自动提高提供给LLMs的指令质量。我们的方法利用LLMs固有的生成能力为给定任务生成多样的候选指令，然后利用在575个现有NLP任务上训练的评分模型对它们进行排名。在对118个领域外任务进行的实验中，Auto-Instruct超越了人工编写的指令和LLM生成指令的现有基线。此外，我们的方法表现出显著的泛化能力，即使对于未纳入其训练过程的其他LLMs也是如此。

English

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.

自动指导：针对黑盒语言模型的自动生成和排序指导

Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models

摘要

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