  In enhancing LinkedIn core content recommendation models, a significant
challenge lies in improving their semantic understanding capabilities. This
paper addresses the problem by leveraging multi-task learning, a method that
has shown promise in various domains. We fine-tune a pre-trained,
transformer-based LLM using multi-task contrastive learning with data from a
diverse set of semantic labeling tasks. We observe positive transfer, leading
to superior performance across all tasks when compared to training
independently on each. Our model outperforms the baseline on zero shot learning
and offers improved multilingual support, highlighting its potential for
broader application. The specialized content embeddings produced by our model
outperform generalized embeddings offered by OpenAI on Linkedin dataset and
tasks. This work provides a robust foundation for vertical teams across
LinkedIn to customize and fine-tune the LLM to their specific applications. Our
work offers insights and best practices for the field to build on.
