Contextual Evaluation of Large Language Models for Classifying Tropical and Infectious Diseases
Abstrak
While large language models (LLMs) have shown promise for medical question answering, there is limited work focused on tropical and infectious disease-specific exploration. We build on an opensource tropical and infectious diseases (TRINDs) dataset, expanding it to include demographic and semantic clinical and consumer augmentations yielding 11000+ prompts. We evaluate LLM performance on these, comparing generalist and medical LLMs, as well as LLM outcomes to human experts. We demonstrate through systematic experimentation, the benefit of contextual information such as demographics, location, gender, risk factors for optimal LLM response. Finally we develop a prototype of TRINDs-LM, a research tool that provides a playground to navigate how context impacts LLM outputs for health.
Penulis (12)
Mercy Asiedu
Nenad Tomasev
Chintan Ghate
Tiya Tiyasirichokchai
Awa Dieng
Oluwatosin Akande
Geoffrey Siwo
Steve Adudans
Sylvanus Aitkins
Odianosen Ehiakhamen
Eric Ndombi
Katherine Heller
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓