Summary of Contextual Evaluation Of Large Language Models For Classifying Tropical and Infectious Diseases, by Mercy Asiedu et al.
Contextual Evaluation of Large Language Models for Classifying Tropical and Infectious Diseases
by Mercy Asiedu, Nenad Tomasev, Chintan Ghate, Tiya Tiyasirichokchai, Awa Dieng, Oluwatosin Akande, Geoffrey Siwo, Steve Adudans, Sylvanus Aitkins, Odianosen Ehiakhamen, Eric Ndombi, Katherine Heller
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This abstract presents a study on applying large language models (LLMs) to medical question answering, specifically focusing on tropical and infectious diseases. The researchers expand an existing dataset, TRINDs, with demographic and semantic clinical and consumer information, resulting in over 11,000 prompts. They evaluate the performance of generalist and medical LLMs on these prompts, comparing it to human expert outcomes. The study demonstrates the importance of contextual information like demographics, location, gender, and risk factors for optimal LLM responses. Finally, they develop a prototype tool, TRINDs-LM, allowing researchers to explore how context affects LLM outputs in health-related tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help answer medical questions related to tropical and infectious diseases. Currently, there isn’t much work done on this topic, so the authors are trying to change that by creating a bigger dataset with more information. They’re testing how well computers can do this job compared to human experts. The study shows that including details like where someone is from or their age helps computers give better answers. This research could lead to a new tool for scientists and doctors to use, helping them understand how computers make decisions in health-related tasks. |
Keywords
» Artificial intelligence » Question answering
