Summary of Rapidly Developing High-quality Instruction Data and Evaluation Benchmark For Large Language Models with Minimal Human Effort: a Case Study on Japanese, by Yikun Sun et al.
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese
by Yikun Sun, Zhen Wan, Nobuhiro Ueda, Sakiko Yahata, Fei Cheng, Chenhui Chu, Sadao Kurohashi
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed self-instruct method leverages GPT-4 to generate Japanese instruction data for serving Large Language Models (LLMs), circumventing the need for massive human annotation. A small amount of English instructions is translated into Japanese, post-edited to achieve native-level quality, and then used as demonstrations by GPT-4 to automatically generate Japanese instruction data. Additionally, an evaluation benchmark containing 80 questions across 8 categories was constructed, with GPT-4 assessing the response quality of LLMs without human references. The results show that models fine-tuned on GPT-4 self-instruct data outperformed Japanese-Alpaca for three base pre-trained models. Furthermore, the LLaMA 13B model defeated GPT-3.5 (Davinci-003) with a 54.37% win-rate. Human evaluation validated the consistency between GPT-4’s assessments and human preference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a way to make instructions for big language models without needing lots of people to do it. They used a machine learning model called GPT-4 to generate Japanese instructions from small amounts of English text. This new method is more efficient than just translating existing resources into Japanese. The team also built an evaluation tool that uses GPT-4 to test how well the language models respond to questions without needing human judges. The results show that using this self-instruct data helps language models work better. |
Keywords
» Artificial intelligence » Gpt » Llama » Machine learning
