Are Large Language Models Effective in Clinical Trial Design? A Study on Baseline Feature Generation
Nafis Neehal, Bowen Wang, Shayom Debopadhaya, Corey Curran, Keerthiram Murugesan, Soham Dan, Vibha Anand, Kristin Bennett
- Anthology ID:
- 2025.findings-naacl.308
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2025
- Month:
- April
- Year:
- 2025
- Address:
- Albuquerque, New Mexico
- Editors:
- Luis Chiruzzo, Alan Ritter, Lu Wang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5557–5570
- Language:
- URL:
- https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.findings-naacl.308/
- DOI:
- Cite (ACL):
- Nafis Neehal, Bowen Wang, Shayom Debopadhaya, Corey Curran, Keerthiram Murugesan, Soham Dan, Vibha Anand, and Kristin Bennett. 2025. Are Large Language Models Effective in Clinical Trial Design? A Study on Baseline Feature Generation. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 5557–5570, Albuquerque, New Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Are Large Language Models Effective in Clinical Trial Design? A Study on Baseline Feature Generation (Neehal et al., Findings 2025)
- PDF:
- https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.findings-naacl.308.pdf