Summary of Lipost: Improved Content Understanding with Effective Use Of Multi-task Contrastive Learning, by Akanksha Bindal et al.
LiPost: Improved Content Understanding With Effective Use of Multi-task Contrastive Learning
by Akanksha Bindal, Sudarshan Ramanujam, Dave Golland, TJ Hazen, Tina Jiang, Fengyu Zhang, Peng Yan
First submitted to arxiv on: 18 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper tackles the challenge of improving semantic understanding in LinkedIn’s core content recommendation models by leveraging multi-task learning, a method that has shown promise across various domains. A pre-trained transformer-based LLM is fine-tuned using multi-task contrastive learning with data from diverse semantic labeling tasks. The results demonstrate positive transfer, leading to superior performance across all tasks compared to training independently on each. The model outperforms the baseline in zero-shot learning and offers improved multilingual support, highlighting its potential for broader applications. The specialized content embeddings produced by the model outperform generalized embeddings offered by OpenAI on LinkedIn datasets and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves LinkedIn’s core content recommendation models by using a special learning method called multi-task learning. This helps the model understand what things mean better. They train a large language model using many different labeling tasks to see if it can learn to do well on all of them. The results show that the model does very well and is good at learning new things without being trained specifically for those things. It also works well with languages other than English, which makes it useful for more people. |
Keywords
» Artificial intelligence » Large language model » Multi task » Transformer » Zero shot
