自動指導：對於黑盒語言模型的自動指令生成和排名

摘要

大型語言模型（LLMs）可以通過遵循自然語言指令執行各種任務，而無需進行特定任務的微調。不幸的是，LLMs 的表現受這些指令的質量影響很大，並且為每個任務手動編寫有效的指令是一個費時且主觀的過程。在本文中，我們介紹了一種名為Auto-Instruct的新方法，用於自動提高提供給LLMs的指令的質量。我們的方法利用LLMs固有的生成能力為給定任務生成多樣的候選指令，然後使用在575個現有NLP任務上訓練的評分模型對它們進行排名。在對118個域外任務進行的實驗中，Auto-Instruct超越了人工編寫的指令和現有的LLM生成的指令基線。此外，我們的方法展現出顯著的泛化能力，即使對於未納入其訓練過程的其他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.

自動指導：對於黑盒語言模型的自動指令生成和排名

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

摘要

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