This paper introduces 26 guiding principles designed to streamline the
process of querying and prompting large language models. Our goal is to
simplify the underlying concepts of formulating questions for various scales of
large language models, examining their abilities, and enhancing user
comprehension on the behaviors of different scales of large language models
when feeding into different prompts. Extensive experiments are conducted on
LLaMA-1/2 (7B, 13B and 70B), GPT-3.5/4 to verify the effectiveness of the
proposed principles on instructions and prompts design. We hope that this work
provides a better guide for researchers working on the prompting of large
language models. Project page is available at
this https URL

本研究介绍了 26 个指导原则，旨在简化大规模语言模型查询和提示的过程，通过对 LLaMA-1/2、GPT-3.5/4 等模型的广泛实验验证所提原则在指令和提示设计上的有效性，以提供更好的指导给从事大规模语言模型提示研究的学者。

基于原则的指导是提问 LLaMA-1/2 和 GPT-3.5/4 所需的全部

Principled Instructions Are All You Need for Questioning LLaMA-1/2,  GPT-3.5/4

Objective: An ethical framework for the use of large language models (LLMs)
is urgently needed to shape how natural language processing (NLP) tools are
used for healthcare applications. Drawing directly from the voices of those
most affected, we propose a set of guiding principles for the use of NLP in
healthcare, with examples based on applications in maternal health.
Materials and Methods: We led an interactive session centered on an LLM-based
chatbot demonstration during a full-day workshop with 39 participants, and
additionally surveyed 30 healthcare workers and 30 birthing people about their
values, needs, and perceptions of AI and LLMs. We conducted quantitative and
qualitative analyses of the interactive discussions to consolidate our findings
into a set of guiding principles.
Results: Using the case study of maternal health, we propose nine principles
for ethical use of LLMs, grouped into three categories: (i) contextual
significance, (ii) measurements, and (iii) who/what is valued. We describe
rationales underlying these principles and provide practical advice.
Discussion: Healthcare faces existing challenges including the balance of
power in clinician-patient relationships, systemic health disparities,
historical injustices, and economic constraints. Our principles serve as a
framework for surfacing key considerations when deploying LLMs in medicine, as
well as providing a methodological pattern for other researchers to follow.
Conclusion: This set of principles can serve as a resource to practitioners
working on maternal health and other healthcare fields to emphasize the
importance of technical nuance, historical context, and inclusive design when
developing LLMs for use in clinical settings.

使用大型语言模型 (Large Language Models，LLMs) 的伦理框架迫切需要，以指导自然语言处理工具在医疗应用中的使用。根据受影响人群的声音，我们提出了一套在医疗中使用自然语言处理的指导原则，以产科健康应用为例。

为医疗自然语言处理设计指导原则：以孕产妇保健为案例研究

Designing Guiding Principles for NLP for Healthcare: A Case Study of  Maternal Health

Data is a crucial infrastructure to how artificial intelligence (AI) systems
learn. However, these systems to date have been largely model-centric, putting
a premium on the model at the expense of the data quality. Data quality issues
beset the performance of AI systems, particularly in downstream deployments and
in real-world applications. Data-centric AI (DCAI) as an emerging concept
brings data, its quality and its dynamism to the forefront in considerations of
AI systems through an iterative and systematic approach. As one of the first
overviews, this article brings together data-centric perspectives and concepts
to outline the foundations of DCAI. It specifically formulates six guiding
principles for researchers and practitioners and gives direction for future
advancement of DCAI.

本文概述了数据中心人工智能 (Data-centric AI) 的概念以及其在数据质量、模型性能、后续部署及实际应用中的作用，阐明了数据驱动的 AI 设计方法，明确了 6 个指导原则，对未来 DCAI 的发展方向做了方向性解释。