# Performance Evaluation of Lightweight Open-source Large Language Models in Pediatric Consultations: A Comparative Analysis

arXiv: https://arxiv.org/abs/2407.15862 · HTML: https://mlsanity.com/paper/2407.15862/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge](https://mlsanity.com/paper/2304.06975/) (2023)
- [Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM](https://mlsanity.com/paper/2403.07816/) (2024)
- [Data-efficient Fine-tuning for LLM-based Recommendation](https://mlsanity.com/paper/2401.17197/) (2024)
- [Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People](https://mlsanity.com/paper/2403.03640/) (2024)
- [Evaluation of ChatGPT-Generated Medical Responses: A Systematic Review and Meta-Analysis](https://mlsanity.com/paper/2310.08410/) (2023)
- [MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications](https://mlsanity.com/paper/2310.15777/) (2023)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated potential applications in medicine, yet data privacy and computational burden limit their deployment in healthcare institutions. Open-source and lightweight versions of LLMs emerge as potential solutions, but their performance, particularly in pediatric settings remains underexplored. In this cross-sectional study, 250 patient consultation questions were randomly selected from a public online medical forum, with 10 questions from each of 25 pediatric departments, spanning from December 1, 2022, to October 30, 2023. Two lightweight open-source LLMs, ChatGLM3-6B and Vicuna-7B, along with a larger-scale model, Vicuna-13B, and the widely-used proprietary ChatGPT-3.5, independently answered these questions in Chinese between November 1, 2023, and November 7, 2023. To assess reproducibility, each inquiry was replicated once. We found that ChatGLM3-6B demonstrated higher accuracy and completeness than Vicuna-13B and Vicuna-7B (P < .001), but all were outperformed by ChatGPT-3.5. ChatGPT-3.5 received the highest ratings in accuracy (65.2%) compared to ChatGLM3-6B (41.2%), Vicuna-13B (11.2%), and Vicuna-7B (4.4%). Similarly, in completeness, ChatGPT-3.5 led (78.4%), followed by ChatGLM3-6B (76.0%), Vicuna-13B (34.8%), and Vicuna-7B (22.0%) in highest ratings. ChatGLM3-6B matched ChatGPT-3.5 in readability, both outperforming Vicuna models (P < .001). In terms of empathy, ChatGPT-3.5 outperformed the lightweight LLMs (P < .001). In safety, all models performed comparably well (P > .05), with over 98.4% of responses being rated as safe. Repetition of inquiries confirmed these findings. In conclusion, Lightweight LLMs demonstrate promising application in pediatric healthcare. However, the observed gap between lightweight and large-scale proprietary LLMs underscores the need for continued development efforts.