# JMedLoRA:Medical Domain Adaptation on Japanese Large Language Models using Instruction-tuning

arXiv: https://arxiv.org/abs/2310.10083 · HTML: https://mlsanity.com/paper/2310.10083/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://mlsanity.com/paper/2308.11483/) (2023)
- [Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations](https://mlsanity.com/paper/2303.18027/) (2023)
- [From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models](https://mlsanity.com/paper/2309.03412/) (2023)

## Built on by (most cited first, 6 of 6)

- [Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training](https://mlsanity.com/paper/2404.10555/) (2024)
- [70B-parameter large language models in Japanese medical question-answering](https://mlsanity.com/paper/2406.14882/) (2024)
- [The Construction of Instruction-tuned LLMs for Finance without Instruction Data Using Continual Pretraining and Model Merging](https://mlsanity.com/paper/2409.19854/) (2024)
- [A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP](https://mlsanity.com/paper/2505.16661/) (2025)
- [Analyzing the Safety of Japanese Large Language Models in Stereotype-Triggering Prompts](https://mlsanity.com/paper/2503.01947/) (2025)
- [Towards Scalable and Cross-Lingual Specialist Language Models for Oncology](https://mlsanity.com/paper/2503.08323/) (2025)

## Abstract (arXiv)

In the ongoing wave of impact driven by large language models (LLMs) like ChatGPT, the adaptation of LLMs to medical domain has emerged as a crucial research frontier. Since mainstream LLMs tend to be designed for general-purpose applications, constructing a medical LLM through domain adaptation is a huge challenge. While instruction-tuning is used to fine-tune some LLMs, its precise roles in domain adaptation remain unknown. Here we show the contribution of LoRA-based instruction-tuning to performance in Japanese medical question-answering tasks. In doing so, we employ a multifaceted evaluation for multiple-choice questions, including scoring based on "Exact match" and "Gestalt distance" in addition to the conventional accuracy. Our findings suggest that LoRA-based instruction-tuning can partially incorporate domain-specific knowledge into LLMs, with larger models demonstrating more pronounced effects. Furthermore, our results underscore the potential of adapting English-centric models for Japanese applications in domain adaptation, while also highlighting the persisting limitations of Japanese-centric models. This initiative represents a pioneering effort in enabling medical institutions to fine-tune and operate models without relying on external services.