AutoHint: Automatic Prompt Optimization with Hint Generation

• URL: http://arxiv.org/abs/2307.07415v2
• Date: Tue, 8 Aug 2023 21:26:53 GMT
• Title: AutoHint: Automatic Prompt Optimization with Hint Generation
• Authors: Hong Sun, Xue Li, Yinchuan Xu, Youkow Homma, Qi Cao, Min Wu, Jian Jiao, Denis Charles
• Abstract summary: This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM) 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.
• Score: 11.737818328656735
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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