Preventing Harmful Content in Model Training

Model Safety and Integrity

By implementing the following measures, Leena AI demonstrates a strong commitment to maintaining the safety and integrity of their AI models throughout the training and fine-tuning processes.

Base Model Training

Leena AI adopts a cautious approach to base model training:

• The company does not engage in pretraining or training of base models.
• This strategy significantly reduces the risk of inadvertently incorporating harmful content during the foundational stages of model development.

Focused Fine-tuning

Leena AI's model development process centers on targeted fine-tuning:

• Fine-tuning is performed exclusively on data curated for specific use cases.
• This focused approach minimizes the likelihood of introducing harmful content during the fine-tuning phase.

Rigorous Data Validation

To further ensure the integrity of their training data, Leena AI employs a comprehensive validation process:

• All training data undergoes scrutiny using the WorkLM Guard system.
• WorkLM Guard is designed to detect and identify potentially harmful content.
• If harmful content is detected, it is promptly removed from the training dataset.

Advanced Content Filtering

For a detailed explanation of the WorkLM Guard system and its role in mitigating harmful content in Language Learning Models (LLMs), please refer to the separate document titled "Mitigating harmful content in LLM."