@inproceedings{hewapathirana2025adapter,
  title={{Adapter-based Fine-tuning for PRIMERA}},
  author={Hewapathirana, Kushan and de Silva, Nisansa and Athuraliya, C. D.},
  booktitle={Applied Data Science \& Artificial Intelligence Symposium},
  month={04},
  year={2025},
  abstract={Multi-document summarisation (MDS) involves generating concise summaries from clusters of related documents. PRIMERA (Pyramid-based Masked Sentence Pre-training for Multi-document Summarisation) is a pre-trained model specifically designed for MDS, utilizing the LED architecture to handle long sequences effectively. Despite its capabilities, fine-tuning PRIMERA for specific tasks remains resource-intensive. To mitigate this, we explore the integration of adapter modules-small, trainable components inserted within transformer layers-that allow models to adapt to new tasks by updating only a fraction of the parameters, thereby reducing computational requirements.},
  doi={10.31705/ADScAI.2025.57},
  misc={https://dl.lib.uom.lk/server/api/core/bitstreams/5bd121c1-3a11-4eb3-b5a4-4bacb770c3b5/content,NLP:ML,https://goo.gl/iY6aTr}
}