Summary of A Role-specific Guided Large Language Model For Ophthalmic Consultation Based on Stylistic Differentiation, by Laiyi Fu et al.
A Role-specific Guided Large Language Model for Ophthalmic Consultation Based on Stylistic Differentiation
by Laiyi Fu, Binbin Fan, Hongkai Du, Yanxiang Feng, Chunhua Li, Huping Song
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach to designing effective dialogues for ophthalmology consultations uses large pre-trained language models, addressing the growing demand for consultations and shortage of ophthalmologists. The traditional fine-tuning strategies are impractical due to increasing model size and neglecting patient-doctor role function during consultations. EyeDoctor, a proposed large language model, enhances accuracy through doctor-patient role perception guided and an augmented knowledge base with external disease information. Experimental results show EyeDoctor achieves higher question-answering precision in ophthalmology consultations, outperforming ChatGPT by 7.25% in Rouge-1 scores and 10.16% in F1 scores on multi-round datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EyeDoctor is a new way to help doctors and patients communicate more effectively about eye problems. It’s like a super smart computer that can answer questions and help with diagnosing and treating eye diseases. The paper shows how EyeDoctor does this better than other similar models, especially when it comes to understanding the roles of the doctor and patient during a consultation. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge base » Large language model » Precision » Question answering » Rouge
