Case Study: GumGum improves LLM fine-tuning efficiency with SuperAnnotate

GumGum boosts labeled data F1 by 10 points with SuperAnnotate

GumGum, a leader in contextual intelligence for digital advertising, faced challenges in maintaining high-quality data annotation and efficiently refining its AI models to ensure precise contextual targeting. To overcome these hurdles in iterative model updates and data quality, the company turned to vendor SuperAnnotate for its data labeling tools and services.

By implementing SuperAnnotate's platform for pre-labeling with LLMs and human review, GumGum streamlined its data workflows. This integration with their existing infrastructure resulted in a ten percentage point increase in the F1 score of their labeled data, significantly enhancing model quality and operational efficiency. SuperAnnotate's solution allowed GumGum to iterate faster and maintain a competitive edge.

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