Aim: To compare students' essay writing performance with or without employing
ChatGPT-3 as a writing assistant tool. Materials and methods: Eighteen students
participated in the study (nine in control and nine in the experimental group
that used ChatGPT-3). We scored essay elements with grades (A-D) and
corresponding numerical values (4-1). We compared essay scores to students'
GPTs, writing time, authenticity, and content similarity. Results: Average
grade was C for both groups; for control (2.39, SD=0.71) and for experimental
(2.00, SD=0.73). None of the predictors affected essay scores: group (P=0.184),
writing duration (P=0.669), module (P=0.388), and GPA (P=0.532). The text
unauthenticity was slightly higher in the experimental group (11.87%, SD=13.45
to 9.96%, SD=9.81%), but the similarity among essays was generally low in the
overall sample (the Jaccard similarity index ranging from 0 to 0.054). In the
experimental group, AI classifier recognized more potential AI-generated texts.
Conclusions: This study found no evidence that using GPT as a writing tool
improves essay quality since the control group outperformed the experimental
group in most parameters.

探究使用 ChatGPT-3 作为写作辅助工具与否对学生的论文写作表现的影响，结果表明，在大多数参数下，控制组优于实验组。

使用 ChatGPT3 作为学生论文写作辅助工具：让您的论文变得更好

Better by you, better than me, chatgpt3 as writing assistance in students essays

If large language models like GPT-3 preferably produce a particular point of
view, they may influence people's opinions on an unknown scale. This study
investigates whether a language-model-powered writing assistant that generates
some opinions more often than others impacts what users write - and what they
think. In an online experiment, we asked participants (N=1,506) to write a post
discussing whether social media is good for society. Treatment group
participants used a language-model-powered writing assistant configured to
argue that social media is good or bad for society. Participants then completed
a social media attitude survey, and independent judges (N=500) evaluated the
opinions expressed in their writing. Using the opinionated language model
affected the opinions expressed in participants' writing and shifted their
opinions in the subsequent attitude survey. We discuss the wider implications
of our results and argue that the opinions built into AI language technologies
need to be monitored and engineered more carefully.

本研究探讨了使用以某种观点为主导的人工智能语言模型的写作助手是否会影响用户的观点，通过在线实验发现，使用偏向一定观点的语言模型会影响用户的写作观点，推荐更加谨慎地监控和设计 AI 语言技术中的观点。

使用带有观点的语言模型共同撰写会影响用户的观点

Co-Writing with Opinionated Language Models Affects Users' Views

Revision is an essential part of the human writing process. It tends to be
strategic, adaptive, and, more importantly, iterative in nature. Despite the
success of large language models on text revision tasks, they are limited to
non-iterative, one-shot revisions. Examining and evaluating the capability of
large language models for making continuous revisions and collaborating with
human writers is a critical step towards building effective writing assistants.
In this work, we present a human-in-the-loop iterative text revision system,
Read, Revise, Repeat (R3), which aims at achieving high quality text revisions
with minimal human efforts by reading model-generated revisions and user
feedbacks, revising documents, and repeating human-machine interactions. In R3,
a text revision model provides text editing suggestions for human writers, who
can accept or reject the suggested edits. The accepted edits are then
incorporated into the model for the next iteration of document revision.
Writers can therefore revise documents iteratively by interacting with the
system and simply accepting/rejecting its suggested edits until the text
revision model stops making further revisions or reaches a predefined maximum
number of revisions. Empirical experiments show that R3 can generate revisions
with comparable acceptance rate to human writers at early revision depths, and
the human-machine interaction can get higher quality revisions with fewer
iterations and edits. The collected human-model interaction dataset and system
code are available at \url{this https URL}. Our system
demonstration is available at \url{this https URL}.

本文介绍了一种人机交互的迭代文本修订系统 (R3)，该系统可提供高质量的文本修改建议，通过人机交互实现文档的迭代修订，从而使大语言模型在文本修订任务中发挥更大作用。