Summary of Epilepsyllm: Domain-specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge, by Xuyang Zhao and Qibin Zhao and Toshihisa Tanaka
EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge
by Xuyang Zhao, Qibin Zhao, Toshihisa Tanaka
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel large language model (LLM) is presented, tailored to address disease-specific challenges in the field of medicine. By fine-tuning a pre-trained LLM with domain-specific datasets, the proposed model, EpilepsyLLM, achieves remarkable performance in providing reliable and specialized medical knowledge responses for epilepsy-related queries in Japanese language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Epilepsy is a major health issue that requires effective diagnosis and treatment. To help doctors and patients better understand this condition, researchers have developed a special computer model called EpilepsyLLM. This model uses huge amounts of data about epilepsy to learn how to provide accurate and helpful answers when asked questions about the disease. |
Keywords
* Artificial intelligence * Fine tuning * Large language model
