Rapidly Developing High-quality Instruction Data and Evaluation
  Benchmark for Large Language Models with Minimal Human Effort: A Case Study
  on Japanese
Yikun Sun Zhen Wan Nobuhiro Ueda Sakiko Yahata Fei Cheng Chenhui Chu Sadao Kurohashi
The creation of instruction data and evaluation benchmarks for serving Large
language models often involves enormous human annotation. This issue becomes
particularly pronounced when rapidly developing such resources for a
non-English language like Japanese. Instead of following the popular practice
of directly translating existing English resources into Japanese (e.g.,
Japanese-Alpaca), we propose an efficient self-instruct method based on GPT-4.
We first translate a small amount of English instructions into Japanese and
post-edit them to obtain native-level quality. GPT-4 then utilizes them as
demonstrations to automatically generate Japanese instruction data. We also
construct an evaluation benchmark containing 80 questions across 8 categories,
using GPT-4 to automatically assess the response quality of LLMs without human
references. The empirical results suggest that the models fine-tuned on our
GPT-4 self-instruct data significantly outperformed the Japanese-Alpaca across
all three base pre-trained models. Our GPT-4 self-instruct data allowed the
LLaMA 13B model to defeat GPT-3.5 (Davinci-003) with a 54.37\% win-rate. The
human evaluation exhibits the consistency between GPT-4's assessments and human
preference. Our high-quality instruction data and evaluation benchmark have
been released here.