Japanese Language Safety Instruction Tuning Dataset

Presenting a synthetic preference dataset for instruction tuning, developed by the LLM-jp collaborative project in Japan. It is specifically aimed at ensuring the safety and appropriateness of large language model outputs in Japanese. The dataset was last updated on February 2, 2025.

Use Cases

• Fine-tune language models for safety alignment using Japanese instruction-response pairs.
• Train preference models to rank outputs based on safety and appropriateness criteria.
• Benchmark the safety performance of Japanese language models on synthetic instruction data.

Strengths

• Developed by the LLM-jp collaborative project, a dedicated initiative for Japanese language models.
• Specifically designed for safety and appropriateness in Japanese LLM outputs.
• Dataset was updated on February 2, 2025, indicating recent maintenance.

Limitations

• The dataset is synthetic, which may not fully capture the complexity of real-world human interactions.
• Key details such as the number of rows, columns, and the specific creation method are currently unknown.
• The license terms are not specified in the provided input, requiring a user to check the repository.

Provenance

Source

LLM-jp collaborative project.

Collection Method

Synthetic dataset creation; specific method details are pending.

Freshness

Last updated on 2025-02-02.

Geography

Japan (focus on Japanese language).