Summary of Lightweight Large Language Model For Medication Enquiry: Med-pal, by Kabilan Elangovan et al.
Lightweight Large Language Model for Medication Enquiry: Med-Pal
by Kabilan Elangovan, Jasmine Chiat Ling Ong, Liyuan Jin, Benjamin Jun Jie Seng, Yu Heng Kwan, Lit Soo Tan, Ryan Jian Zhong, Justina Koi Li Ma, YuHe Ke, Nan Liu, Kathleen M Giacomini, Daniel Shu Wei Ting
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper presents Med-Pal, a medication domain-specific Large Language Model (LLM)-chatbot designed to assist patient education in digital health development. The team trained and validated Med-Pal using a fine-grained dataset from five lightweight LLMs with smaller parameter sizes (7 billion or less) to prioritize operational efficiency. A clinical evaluation was conducted using the SCORE criteria, focusing on safety, accuracy, bias, reproducibility, and ease of understanding. The best-performing light-weighted LLM, Mistral-7b, was chosen as Med-Pal’s backbone LLM. This chatbot was validated on an independent dataset for a broad range of medication-related questions (231 in total), demonstrating high-quality responses in accuracy and safety domains. Compared to existing LLMs like Biomistral and Meerkat, Med-Pal outperformed in generating patient-friendly responses with reduced bias and errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new tool called Med-Pal that helps people understand medication instructions better. The team used special computer models to train Med-Pal, making it more efficient and accurate than other similar tools. They tested Med-Pal on many different questions about medications and found that it did a great job of providing helpful answers. Compared to other tools, Med-Pal was especially good at explaining things in a way that’s easy for patients to understand. This new tool can be very useful in improving healthcare communications. |
Keywords
» Artificial intelligence » Large language model
