  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.
