RISEN Framework and Prompt Engineering Technique applied to each LLM model.

RISEN Framework and Prompt Engineering Technique applied to each LLM model.

<p>RISEN Framework and Prompt Engineering Technique applied to each LLM model.</p>

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Bibliographic Details
Main Author:	Bazen Gashaw Teferra (21799468) (author)
Other Authors:	Argyrios Perivolaris (21799471) (author), Wei-Ni Hsiang (21799474) (author), Christian Kevin Sidharta (21799477) (author), Alice Rueda (15399721) (author), Karisa Parkington (21799480) (author), Yuqi Wu (2203138) (author), Achint Soni (21799483) (author), Reza Samavi (4266001) (author), Rakesh Jetly (729659) (author), Yanbo Zhang (184664) (author), Bo Cao (552407) (author), Sirisha Rambhatla (21799486) (author), Sri Krishnan (21799489) (author), Venkat Bhat (11814479) (author)
Published:	2025
Subjects:	Biotechnology Sociology Science Policy Mental Health Biological Sciences not elsewhere classified textual interview data natural language processing contains textual data assessing item scales large language models reported questionnaire scores individual &# 8217 shot learning llms promising preliminary performance various models indicates including gpt models depression using llms 8 items related gpt models 8 items tuning models various llms reported experiences related symptoms various factors especially gpt eight items 8 scale individual phq subjective response study provides report questionnaires questionnaires rely project aims often lead nuanced difficulties novel outlook help mitigate f1 score daily functioning consistently better common practice cognitive states clinical settings clinical interviews challenges involved assessed based additional datasets 9 %), 74 ).
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