Synthetic Data Generation: A Game-Changer for Large Language Models

Synthetic data generation is revolutionizing the field of machine learning, enabling researchers to create vast datasets when real-world data is limited and expensive. This technique allows machine learning models to be trained more effectively, enhancing their performance across various applications. However, integrating synthetic data into machine learning models presents several challenges, particularly regarding the biases and attributes the synthetic data may introduce.

Large language models (LLMs) are sensitive to the properties of the artificial data they are trained on.

Understanding how these inherited characteristics impact the behavior and performance of LLMs is crucial. The primary concern is whether the synthetic data can introduce unintended biases or other attributes that might affect the model’s outputs. This understanding is vital for ensuring that models trained with synthetic data are effective and fair, avoiding perpetuating negative traits from the data generation process.

Current methods for optimizing the data space involve data augmentation, pseudo-labeling, data weighting, data pruning, and curriculum learning. Despite their utility, these methods are limited by the properties inherent in the initial datasets. They often need to be able to introduce new, desirable attributes, restricting their effectiveness in optimizing models for specific characteristics.

“The generated data is crafted to exhibit specific characteristics beneficial for the models’ learning process.” - Researchers from Cohere for AI

Researchers from Cohere for AI and Cohere have proposed a novel concept called “active inheritance.” This method aims to intentionally steer synthetic data generation towards specific non-differentiable objectives, such as high lexical diversity and low toxicity. By guiding the data generation process, researchers can directly influence the characteristics of the resulting models.

Active inheritance involves selecting proxy labels based on desired characteristics, generating multiple samples for each prompt, and choosing the sample that maximizes the desired attribute.

The active inheritance method has shown significant promise. For instance, targeted sampling effectively steers model behavior towards desirable attributes, resulting in substantial improvements. Models demonstrated up to 116% improvement in length and 43% enhancement in linguistic diversity. Moreover, the method reduced toxicity by up to 40%. These results highlight the potential of active inheritance to enhance the quality and safety of language models.

In conclusion, the research underscores the significant impact of synthetic data on the attributes of LLMs. By introducing the concept of active inheritance, researchers from Cohere have provided a robust framework for steering synthetic data generation towards desirable characteristics. This method enhances specific attributes, such as lexical diversity and reduced toxicity, ensuring that models trained with synthetic data are effective and safe.

The study’s results demonstrate that it is possible to successfully and efficiently instill desired attributes into a model’s generation with minimal effort.