# IvyGPT: InteractiVe Chinese pathwaY language model in medical domain

arXiv: https://arxiv.org/abs/2307.10512 · HTML: https://mlsanity.com/paper/2307.10512/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Medical LLM alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Medical LLM alignment](https://mlsanity.com/alignment/medical-llm-alignment/)

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [HuatuoGPT, towards Taming Language Model to Be a Doctor](https://mlsanity.com/paper/2305.15075/) (2023)
- [HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge](https://mlsanity.com/paper/2304.06975/) (2023)

## Built on by (most cited first, 9 of 9)

- [CMB: A Comprehensive Medical Benchmark in Chinese](https://mlsanity.com/paper/2308.08833/) (2023)
- [A Survey on LoRA of Large Language Models](https://mlsanity.com/paper/2407.11046/) (2024)
- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
- [Data-Centric Foundation Models in Computational Healthcare: A Survey](https://mlsanity.com/paper/2401.02458/) (2024)
- [HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses](https://mlsanity.com/paper/2312.15883/) (2023)
- [Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning](https://mlsanity.com/paper/2509.19090/) (2025)
- [Fine-Tuning Medical Language Models for Enhanced Long-Contextual Understanding and Domain Expertise](https://mlsanity.com/paper/2407.11536/) (2024)
- [A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification](https://mlsanity.com/paper/2311.14388/) (2023)
- [AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning](https://mlsanity.com/paper/2407.07094/) (2024)

## Abstract (arXiv)

General large language models (LLMs) such as ChatGPT have shown remarkable success. However, such LLMs have not been widely adopted for medical purposes, due to poor accuracy and inability to provide medical advice. We propose IvyGPT, an LLM based on LLaMA that is trained and fine-tuned with high-quality medical question-answer (QA) instances and Reinforcement Learning from Human Feedback (RLHF). After supervised fine-tuning, IvyGPT has good multi-turn conversation capabilities, but it cannot perform like a doctor in other aspects, such as comprehensive diagnosis. Through RLHF, IvyGPT can output richer diagnosis and treatment answers that are closer to human. In the training, we used QLoRA to train 33 billion parameters on a small number of NVIDIA A100 (80GB) GPUs. Experimental results show that IvyGPT has outperformed other medical GPT models.