Overview - Biomedical Generative Retrieval (BioGen) Track 2024ΒΆ
Proceedings | Data | Runs | Participants
Large language models (LLMs) adapted for the biomedical domain show exceptional performance on many tasks, but are also known to provide false information, i.e., hallucinations or confabulations. Inaccuracies may be particularly harmful in high-risk situations, such as making clinical decisions or appraising biomedical research. The TREC 2024 BioGen task will focus on reference attribution as a means to mitigate generation of false statements by LLMs. The goal of the TREC 2024 BioGen task will be to cite references to support the text of the sentences and the overall answer from LLM output for each topic. Each run will be scored by the proportion of sentences and overall answer that have correctly supporting attributions.
Track coordinator(s):
- Bill Hersh, Oregon Health & Science University
- Dina Demner-Fushman, National Library of Medicine
- Deepak Gupta, National Library of Medicine
- Steven Bedrick, Oregon Health & Science University
- Kirk Roberts, University of Texas Houston
Track Web Page: https://dmice.ohsu.edu/trec-biogen/task.html