LinkedIn, a pioneer in AI recommender systems for over 15 years, recently faced the challenge of upgrading its recommendation technology to better serve future job seekers. The company realized that traditional prompting methods wouldn’t meet their goals for accuracy, speed, and efficiency. Instead, LinkedIn developed a novel approach involving multi-teacher distillation, starting with a massive 7-billion-parameter model fine-tuned using a detailed product policy document. This document, created in collaboration between product managers and engineers, scored thousands of job description and profile pairs to align the model with LinkedIn’s product standards.
The process involved training two specialized teacher models: one focused on product policy adherence and another on click prediction and personalization. These informed the development of a streamlined 1.7 billion parameter student model that balances quality and performance. This approach not only improved recommendation outcomes but also modularized training methods, allowing independent iteration on different model objectives.
Berger, LinkedIn’s VP of product engineering, emphasizes the importance of integrating product expertise with machine learning development, marking a shift in how teams collaborate on AI projects. The multi-teacher distillation technique has become a core methodology at LinkedIn, driving significant improvements in recommendation quality and serving as a blueprint for future AI products. The company also highlights the value of flexible pipelines and traditional engineering debugging to accelerate R&D.
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