Summary of Technical Report: Small Language Model For Japanese Clinical and Medicine, by Shogo Watanabe
Technical Report: Small Language Model for Japanese Clinical and Medicine
by Shogo Watanabe
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 The paper presents a small language model (SLM) called NCVC-slm-1, trained on high-quality Japanese text with a focus on clinical and medicine content. The 1B-parameter model uses a specialized morphological analyzer and tokenizer to perform various tasks, including text generation and understanding clinical text. Fine-tuning NCVC-slm-1 achieves the highest scores on six out of eight tasks in the JMED-LLM benchmark, demonstrating its feasibility for downstream applications in the field. The model’s performance highlights its potential to accelerate and develop clinical and medicine research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new language model called NCVC-slm-1 that helps understand Japanese medical texts. It was trained on high-quality text and taught to recognize different diseases, medicines, and tests. This small model can do more than just generate text – it can also comprehend clinical text. The researchers tested the model and found it performed better than other big models in some areas. They hope this new tool will help make medical research faster and more efficient. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Text generation » Tokenizer
