[论文解读] Development of the ChatGPT, Generative Artificial Intelligence and Natural Large Language Models for Accountable Reporting and Use (CANGARU) Guidelines

Giovanni Cacciamani, Michael Eppler+5 more|arXiv (Cornell University)|Jul 18, 2023

Artificial Intelligence in Healthcare and Education被引用 14

一句话总结

这篇论文提出了 CANGARU 计划，通过四-part 协议为学术界在伦理使用、披露和报告 GAI/GPT/LLM 制定全球跨学科指南。

ABSTRACT

The swift progress and ubiquitous adoption of Generative AI (GAI), Generative Pre-trained Transformers (GPTs), and large language models (LLMs) like ChatGPT, have spurred queries about their ethical application, use, and disclosure in scholarly research and scientific productions. A few publishers and journals have recently created their own sets of rules; however, the absence of a unified approach may lead to a 'Babel Tower Effect,' potentially resulting in confusion rather than desired standardization. In response to this, we present the ChatGPT, Generative Artificial Intelligence, and Natural Large Language Models for Accountable Reporting and Use Guidelines (CANGARU) initiative, with the aim of fostering a cross-disciplinary global inclusive consensus on the ethical use, disclosure, and proper reporting of GAI/GPT/LLM technologies in academia. The present protocol consists of four distinct parts: a) an ongoing systematic review of GAI/GPT/LLM applications to understand the linked ideas, findings, and reporting standards in scholarly research, and to formulate guidelines for its use and disclosure, b) a bibliometric analysis of existing author guidelines in journals that mention GAI/GPT/LLM, with the goal of evaluating existing guidelines, analyzing the disparity in their recommendations, and identifying common rules that can be brought into the Delphi consensus process, c) a Delphi survey to establish agreement on the items for the guidelines, ensuring principled GAI/GPT/LLM use, disclosure, and reporting in academia, and d) the subsequent development and dissemination of the finalized guidelines and their supplementary explanation and elaboration documents.

研究动机与目标

• 促使在学术界对 GAI/GPT/LLM 的伦理使用与披露建立统一的跨学科标准。
• 介绍 CANGARU 计划及其促进全球包容性共识的目标。
• 概述用于制定、验证和传播指南的四部分协议。
• 解决因出版商规则和报告实践不一致而产生的巴别塔效应的风险。

提出的方法

• 四部分协议：对学术研究中 GAI/GPT/LLM 应用及报告标准的持续系统综述。
• 对提及 GAI/GPT/LLM 的期刊现有作者指南进行文献计量分析，以评估差异并识别共同规则。
• Delphi 调查，以就确保原则性使用、披露和报告的指南条款达成共识。
• 制定并传播最终指南，附带解释与阐述性文件。

实验结果

研究问题

• RQ1 当前在学术研究中与 GAI/GPT/LLM 相关的报告标准和观点有哪些？
• RQ2 提及 GAI/GPT/LLM 的期刊中，现有作者指南如何差异化，又出现了哪些共同规则？
• RQ3 应构成 CANGARU 指南的就学术界伦理使用和披露 GAI/GPT/LLM 的共识条款有哪些？
• RQ4 如何将最终指南有效传播并在各学科中采纳？

主要发现

• 论文为在学术界开发可问责的 GAI/GPT/LLM 报告指南定义了四部分协议。
• 它提出系统综述、文献计量分析、Delphi 调查和传播计划作为核心方法。
• 该方法旨通过协调分散的出版商规则来防止巴别塔效应。
• 该倡议旨在在学术工作中就 GAI/GPT/LLM 的伦理使用、披露和报告建立跨学科全球包容性共识。