我们提出了一个概念上的观点提示大语言模型（LLM），区分（1）diegetic提示（叙事的一部分，例如：“从前，我看到一只狐狸.），以及（2）非diegetic提示（外部，例如“写关于狐狸的冒险。").有了这个镜头，我们研究了129多产人群工作者如何用不同的用户界面（1 vs 3建议，有/没有非diegetic提示;实现GPT-3）写短文本：当界面提供多个建议，并提供了一个选项，非diegetic提示，参与者更喜欢从多个建议中选择，而不是通过非diegetic提示控制它们。当参与者提供非diegetic提示，这是要求灵感，主题或事实。特别是单一的建议，指导与diegetic和非diegetic信息。这项工作通过揭示（1）编写非diegetic提示需要努力，（2）人们结合diegetic和非diegetic提示，（3）他们使用他们的草稿（即diegetic信息）和建议时间来战略性地指导LLM，从而告知人类与人工智能的交互。
英文摘要：
We propose a conceptual perspective on prompts for Large Language Models (LLMs) that distinguishes between (1) diegetic prompts (part of the narrative, e.g. "Once upon a time, I saw a fox..."), and (2) non-diegetic prompts (external, e.g. "Write about the adventures of the fox."). With this lens, we study how 129 crowd workers on Prolific write short texts with different user interfaces (1 vs 3 suggestions, with/out non-diegetic prompts; implemented with GPT-3): When the interface offered multiple suggestions and provided an option for non-diegetic prompting, participants preferred choosing from multiple suggestions over controlling them via non-diegetic prompts. When participants provided non-diegetic prompts it was to ask for inspiration, topics or facts. Single suggestions in particular were guided both with diegetic and non-diegetic information. This work informs human-AI interaction with generative models by revealing that (1) writing non-diegetic prompts requires effort, (2) people combine diegetic and non-diegetic prompting, and (3) they use their draft (i.e. diegetic information) and suggestion timing to strategically guide LLMs.
A Sentiment Consolidation Framework for Meta-Review Generation
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）
GPT is becoming a Turing machine: Here are some ways to program it
Fred Hutchinson Cancer Research Center（弗雷德·哈钦森癌症研究中心）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Microsoft Research（微软研究院）
Iowa State University（爱荷华州立大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of California, Merced（加州大学默塞德分校）；Argonne National Laboratory（阿贡国家实验室）
LitLLMs, LLMs for Literature Review: Are we there yet?
ServiceNow Research（ServiceNow研究院）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；HEC Montreal（蒙特利尔高等商学院）；University of Waterloo（滑铁卢大学）；University of British Columbia（不列颠哥伦比亚大学）；Polytechnique Montreal（蒙特利尔理工学院）
