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Opportunities and Pitfalls with Large Language Models for Biomedical Annotation (LLM-BA)
Important Dates
Aug 1, 2024 Call for talks deadline
Sept 9, 2024 Notification of acceptance
Dec 2, 2024 Poster abstract submission deadline
Jan 4-8, 2025 Conference dates

LLMs and biomedical annotations have a symbiotic relationship. LLMs rely on high-quality annotations for training and improvement, while they can also automate parts of the annotation process and improve its quality.

High-quality, well-annotated biomedical data is crucial for training LLMs to understand and process scientific information. These annotations can include labeling entities (genes, proteins), relations (interactions), and other relevant information. By incorporating annotated data, LLMs can learn specific domain knowledge and improve their accuracy in tasks like information extraction, knowledge base creation, and text summarization. Diverse and unbiased annotations can help mitigate bias in LLMs, ensuring their outputs are fair and representative of the underlying data.

LLMs can be used to automate some aspects of annotation, such as identifying potential entities or suggesting relevant relations. This can significantly reduce the workload for human annotators. LLMs can identify areas of uncertainty in the data and suggest which annotations would be most valuable for improving their performance. This creates a feedback loop where LLMs guide the annotation process for optimal results. Finally, LLMs can be used to check the consistency and accuracy of annotations, identifying potential errors or inconsistencies.

By addressing these challenges this workshop aims to clarify the potential and limits of LLMs in advancing biomedical research and knowledge discovery.

Workshop Programs


Fabio Rinaldi, PhD
Dalle Molle Institute for Artificial Intelligence, IDSIA USI-SUPSI, Lugano, Switzerland
Jin-Dong Kim, PhD
Database Center for Life Sciences, DBCLS/ROIS, Kashiwa, Japan
Cecilia Arighi, PhD
Research Associate Professor
University of Delaware
Zhiyong Lu, PhD, FACMI
Senior Investigator