Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning

Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning | Signal Canvas | ScienceToStartup

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Proof freshness: stale
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Display score: 7/10
Last proof check: 2026-03-24
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 50%

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/parameter-efficient-fine-tuning-for-medical-text-summarization-a-comparative-study-of-lora-prompt-tuning-and-full-fine-t

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source_context

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Evidence Receipt

Route status: building

Claims: 0

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning

PDF: https://arxiv.org/pdf/2603.21970v1

Repository: https://github.com/eracoding/llm-medical-summarization

Source count: Pending verification

Coverage: 50%

Last proof check: 2026-03-24T21:26:52.285Z

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