Workshop Overview
SLM4Health focuses on exploring the role and potential of Small Language Models (SLMs) in healthcare-related natural language processing (NLP) tasks.
As SLMs gain traction in clinical settings due to their adaptability, efficiency, and lower resource demands, they offer a promising alternative to larger models, especially in resource-constrained environments. The workshop will address challenges such as performance trade-offs and ethical concerns including bias, privacy, and interpretability.
We aim to bring together researchers and practitioners to discuss SLM applications in clinical tasks, compare them with large language models, and explore methods to overcome these challenges, ultimately improving patient care and clinician support through more tailored NLP tools.
Key Topics
- Clinical information extraction using SLMs
- Multilingual adaptation in healthcare NLP
- Responsible AI practices for clinical SLMs
- Performance comparison: SLMs vs LLMs
- Real-world healthcare case studies
- Ethical considerations in clinical NLP
Organizers
- • Kerstin Denecke (Bern University of Applied Sciences, Switzerland)
- • Daniel Reichenpfader (University of Geneva, BFH, Switzerland)
- • Yihan Deng (University of Bern, BFH, Switzerland)
- • Douglas Teodoro (University of Geneva, Switzerland)
- • Edward Choi (KAIST, South Korea)
Submission Guidelines
Paper Formats
- 8-page long paper (Springer LNCS format)
- 4-page short paper (Springer LNCS format)
Templates available at Springer LNCS website
Important Dates
- 📅 Submission deadline: April 15, 2025
- 📅 Notification: May 3, 2025
- 📅 Camera-ready: May 15, 2025