Comparison of Prompt Engineering and Fine-Tuning Strategies in Large Language Models in the Classification of Clinical Notes Article Swipe
Xiaodan Zhang
,
Nabasmita Talukdar
,
Sandeep Vemulapalli
,
Sumyeong Ahn
,
Jiankun Wang
,
Meng Han
,
Sardar Mehtab Bin Murtaza
,
Dmitry Leshchiner
,
Aakash Ajay Dave
,
Dimitri F. Joseph
,
Martin Witteveen‐Lane
,
Dave Chesla
,
Jiayu Zhou
,
Bin Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.02.07.24302444
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.02.07.24302444
The emerging large language models (LLMs) are actively evaluated in various fields including healthcare. Most studies have focused on established benchmarks and standard parameters; however, the variation and impact of prompt engineering and fine-tuning strategies have not been fully explored. This study benchmarks GPT-3.5 Turbo, GPT-4, and Llama-7B against BERT models and medical fellows’ annotations in identifying patients with metastatic cancer from discharge summaries. Results revealed that clear, concise prompts incorporating reasoning steps significantly enhanced performance. GPT-4 exhibited superior performance among all models. Notably, one-shot learning and fine-tuning provided no incremental benefit. The model’s accuracy sustained even when keywords for metastatic cancer were removed or when half of the input tokens were randomly discarded. These findings underscore GPT-4’s potential to substitute specialized models, such as PubMedBERT, through strategic prompt engineering, and suggest opportunities to improve open-source models, which are better suited to use in clinical settings.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.02.07.24302444
- https://www.medrxiv.org/content/medrxiv/early/2024/02/08/2024.02.07.24302444.full.pdf
- OA Status
- green
- Cited By
- 29
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391640544
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391640544Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.02.07.24302444Digital Object Identifier
- Title
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Comparison of Prompt Engineering and Fine-Tuning Strategies in Large Language Models in the Classification of Clinical NotesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-02-08Full publication date if available
- Authors
-
Xiaodan Zhang, Nabasmita Talukdar, Sandeep Vemulapalli, Sumyeong Ahn, Jiankun Wang, Meng Han, Sardar Mehtab Bin Murtaza, Dmitry Leshchiner, Aakash Ajay Dave, Dimitri F. Joseph, Martin Witteveen‐Lane, Dave Chesla, Jiayu Zhou, Bin ChenList of authors in order
- Landing page
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https://doi.org/10.1101/2024.02.07.24302444Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2024/02/08/2024.02.07.24302444.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2024/02/08/2024.02.07.24302444.full.pdfDirect OA link when available
- Concepts
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Linguistics, Computer science, Natural language processing, Artificial intelligence, Mathematics education, Psychology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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29Total citation count in OpenAlex
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2025: 21, 2024: 8Per-year citation counts (last 5 years)
- References (count)
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15Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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