# CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring
Language Model Capabilities

Mina Lee<EMAIL_ADDRESS>Stanford UniversityUnited States ,  Percy
Liang<EMAIL_ADDRESS>Stanford UniversityUnited States and  Qian Yang
<EMAIL_ADDRESS>Cornell UniversityUnited States

(2022)

###### Abstract.

Large language models (LMs) offer unprecedented language generation
capabilities and exciting opportunities for interaction design. However, their
highly context-dependent capabilities are difficult to grasp and are often
subjectively interpreted. In this paper, we argue that by curating and
analyzing large interaction datasets, the HCI community can foster more
incisive examinations of LMs’ generative capabilities. Exemplifying this
approach, we present CoAuthor, a dataset designed for revealing GPT-3’s
capabilities in assisting creative and argumentative writing. CoAuthor
captures rich interactions between 63 writers and four instances of GPT-3
across 1445 writing sessions. We demonstrate that CoAuthor can address
questions about GPT-3’s language, ideation, and collaboration capabilities,
and reveal its contribution as a writing “collaborator” under various
definitions of good collaboration. Finally, we discuss how this work may
facilitate a more principled discussion around LMs’ promises and pitfalls in
relation to interaction design. The dataset and an interface for replaying the
writing sessions are publicly available at https://coauthor.stanford.edu.

Human-AI collaborative writing, GPT-3, language models, dataset,
crowdsourcing, natural language generation, writing assistants.

††copyright: acmcopyright††journalyear: 2022††copyright:
acmcopyright††conference: CHI Conference on Human Factors in Computing
Systems; April 29-May 5, 2022; New Orleans, LA, USA††booktitle: CHI Conference
on Human Factors in Computing Systems (CHI ’22), April 29-May 5, 2022, New
Orleans, LA, USA††price: 15.00††doi: 10.1145/3491102.3502030††isbn:
978-1-4503-9157-3/22/04††ccs: Human-centered computing Human computer
interaction (HCI)††ccs: Computing methodologies Natural language generation

Figure 1. We present CoAuthor, a dataset designed for revealing GPT-3’s
generative capabilities for interactive writing. It contains rich interactions
between 63 writers and 4 instances of GPT-3 across 1445 writing sessions. Each
session starts with a prompt (black text). Writers then freely write (brown),
request suggestions from GPT-3 (blue), accept or dismiss suggestions, and edit
accepted suggestions or previous texts in any order they choose.

Three images labelled as “Write,” “Get suggestions,” and “Edit” show a
possible sequence of interactions between a writer and GPT-3 in CoAuthor.
Image “Write” shows a writing prompt and text written by the writer. Image
“Get suggestions” shows a list of five suggestions generated by GPT-3. Image
“Edit” shows text edited by the writer.

##  1\. Introduction

Large language models (LMs) offer exciting opportunities for novel interaction
design. Recent LMs (such as GPT-2 (Radford et al., 2019), GPT-3 (Brown et al.,
2020), GPT-J (Wang and Komatsuzaki, 2021), Jurassic-1 (Lieber et al., 2021),
Megatron-Turing-NLG (Kharya and Alvi, 2021), and Gopher (Rae et al., 2021))
can generate a wide variety of prose and dialogues with an unprecedented level
of fluency out of the box. Through fine-tuning, these models can further
become specialized at particular tasks, such as composing emails (Buschek et
al., 2021) or providing health consultation (Wang et al., 2021). As a result,
the HCI community has become interested in the opportunities surrounding LMs’
generative capabilities. Some have started leveraging off-the-shelf LMs for
rapid prototyping of novel natural language interactions (Wu et al., 2021);
others have started crafting end-user-facing applications with fine-tuned LMs
directly111More examples of end-user-facing LM applications exist outside
academic research, such as GPT-3-powered copywriting tools (e.g. Copy.ai,
Copysmith, Omneky, Jarvis, Writesonic), creative writing tools (AI Dungeon, AI
Writer, ShortlyAI, Rytr, QuillBot), and programming tools (e.g.TabNine,
Copilot).(Yang et al., 2020b), even though how soon such applications can
become production-ready remain highly disputable (Gehman et al., 2020; Abid et
al., 2021).

Harnessing LMs’ generative capabilities to power interaction designs begins
with a holistic understanding of these capabilities (Bly and Churchill, 1999;
Yang et al., 2020a); this includes understanding what LMs can and cannot do
under diverse interaction contexts. For example, when designing the mode of
interaction between writers and GPT-3 for writing assistants, designers may
ask: Can GPT-3 contribute new ideas to one’s writing, or does it merely expand
on existing ideas? Does this ideation capability differ in the context of
writing fictional stories versus persuasive arguments? To what extent does
this capability fluctuate when its decoding parameters change? Answers to such
questions guide early interaction design process. Without them, envisioning
how an LM may serve writers’ needs—or when and how it may fall short—becomes a
shot in the dark.

In this paper, we investigate how HCI researchers can examine LMs’ generative
capabilities to inform interaction design. Any LM’s performance fluctuates
significantly depending on the preceding text (Liu et al., 2021), decoding
parameters (Hashimoto et al., 2019), among other factors. Examining such
variable capabilities requires more than interviewing its users (Yang et al.,
2019) or tinkering with the model. The challenge becomes even more salient in
interactive settings: After a writer and a model takes turns in writing a
story and iteratively edits it, how can one tease out and characterize the
model’s contribution to the writing, or how well it served the writer’s needs?

This paper proposes curating and analyzing large interaction datasets as one
way to address these challenges. Datasets have long been useful in evaluating
LMs in Natural Language Processing (NLP) research (Rajpurkar et al., 2018;
Wang et al., 2018; Gehrmann et al., 2021; Kiela et al., 2021). We argue that,
when thoughtfully designed for HCI, datasets can also reveal what LMs can do
for interaction design (Section 3). Exemplifying this approach, we present
CoAuthor, a dataset designed for revealing GPT-3’s generative capabilities in
assisting creative and argumentative writing. It captures rich interactions
between 63 writers and four instances of GPT-3 across 1445 writing sessions
(Section 4).

We demonstrate that CoAuthor can help to answer high-level questions about
GPT-3’s generative capabilities. Specifically, we reason about its language
capabilities (ability to generate fluent text), ideation capabilities (ability
to generate new ideas), and collaboration capabilities (ability to work
jointly with writers) using CoAuthor (Section 5.1). The dataset can also help
researchers investigate GPT-3’s contribution as a writing “collaborator” under
various definitions of good collaboration (Section 5.2). We provide a tool for
replaying all writing sessions in CoAuthor, giving designers a felt
understanding of the interactions. The dataset and a replay interface are
publicly available at https://coauthor.stanford.edu.

This paper makes three contributions. First, it identifies a need for holistic
understanding of LMs’ generative capabilities for interaction design. Second,
it proposes curating and analyzing large interaction datasets as a viable
approach to making LMs’ generative capabilities more accessible to the HCI
community; this opens up new research opportunities in designing and mining
large interaction datasets as a research contribution. Finally, CoAuthor
offers a vivid depiction of GPT-3’s capabilities in assisting creative and
argumentative writing, facilitating a more principled discussion around
GPT-3’s promises and pitfalls in interaction design.

##  2\. Related Work

###  2.1. Understanding Technological Capabilities

####  2.1.1. Types of Understanding

Appropriate interaction design for a new technology requires a deep
understanding of its capabilities and limitations. Concretely, this
understanding serves two purposes (Buxton, 2010):

  * •

A specific, felt capability understanding concerns how the technology can help
users in particular contexts, how that interaction may unfold, and what user
experiences it may entail. It guides designers in making detailed interaction
and user experience (UX) design choices (Yang et al., 2019).

  * •

A holistic capability understanding concerns what the technology is capable
and incapable of doing broadly, across various contexts. It gives structure to
designers’ considerations around how the technology may provide value to
different users and what guardrails are necessary to ensure its appropriate
use (Gaver, 1991; Ozenc et al., 2010).

####  2.1.2. Ways to Develop Understandings

Researchers have created systems to help designers grasp the capabilities of
new or partially-understood technologies. For example, Arduino made accessible
the interaction design possibilities of sensors and motors (Martelaro et al.,
2020); Wekinator (Fiebrink and Cook, 2010) and Teachable Machine (Carney et
al., 2020) made accessible the otherwise abstract capabilities of supervised
machine learning classifiers. This approach enables designers to tinker with
the technology easily and repeatedly to gain a specific, felt understanding.
In addition, designers can use replay enactment (Holstein et al., 2020) to
further materialize the dynamics of interactions between users and systems and
make complex system behavior more tangible. By “tinkering with a scale”
(observing how systems react to different user inputs) and potentially
replaying many interactions, designers can develop a more holistic
understanding of what the technology can and cannot do broadly (Schön, 1984;
Lim et al., 2008). This paper adds to this line of research by curating a
large interaction dataset that can be replayed to provide both holistic and
felt understandings.

###  2.2. Understanding Language Models’ Generative Capabilities

####  2.2.1. Language Models’ Generative Capabilities

The goal of Natural Language Generation (NLG) is to produce fluent text in
many domains, such as machine translation (Fan et al., 2020), summarization
(Lewis et al., 2020), dialogue (Hosseini-Asl et al., 2020), style transfer
(Dathathri et al., 2020), and programming code (Chen et al., 2021). In recent
years, building large LMs has become a common approach to NLG. Unlike
traditional models designed to perform a single task (e.g. do translation or
summarization), recent LMs (Brown et al., 2020; Wang and Komatsuzaki, 2021;
Lieber et al., 2021; Kharya and Alvi, 2021; Rae et al., 2021)—the ones that
this paper focuses on—learn task-agnostic language representations through
pre-training. These LMs can power vastly different tasks (e.g. do translation
and summarization) (Lewis et al., 2020). Through additional fine-tuning, pre-
trained LMs can further be specialized to given tasks and contexts (e.g.
composing emails (Buschek et al., 2021) or providing health consultation (Wang
et al., 2021)). It would be naïve, however, to think that these LMs have
achieved language mastery, or can be trusted with all tasks and contexts. Pre-
trained on massive amounts of text on the Internet, LMs are known to produce
linguistically flawed, factually incorrect, or even ethically problematic text
at times (Gehman et al., 2020; Abid et al., 2021). Guardrails are necessary
when using these models.

####  2.2.2. Challenges in Understanding Generative Capabilities

Understanding LMs’ generative capabilities for interactive writing is
challenging for at least two reasons:

  * •

LMs’ generative capabilities are highly context-dependent; therefore, it is
difficult to cover all contexts. It may be tempting to describe, for example,
GPT-3 as capable of assisting users with writing, coding, and carrying a
dialogue. But considering all contexts and characterizing when and how GPT-3
succeed or fail to perform the tasks is effectively intractable.
Overestimation of what LMs can thwart interaction design (Yang et al., 2019).

  * •

LMs’ generative capabilities can be subjectively interpreted; therefore, they
are susceptible to varying evaluations even within a given interaction
context. As authors, we can all resonate with how difficult it is to assess
how a co-author has helped us with writing a paper. Formally, this assessment
requires analysis based on various definitions of good collaboration, at
multiple levels of abstraction.222For example, a human writing collaborator
can enhance the fluency and the sense of audience in the writing (contribution
at a text production level) (Boch, 2007; Storch, 2005), can expand the pool of
knowledge and ideas (ideation and thinking) (Donato, 1994), can better harness
the socialization opportunity with the discourse communities (socialization)
(Yang, 2014; Yim and Warschauer, 2017), and more. On an interaction level,
good writing collaboration can exhibit different interaction patterns, such as
different levels of mutuality and equality (Abrams, 2019).Assessing the
functional and experiential value of machine-generated text shares similar
complexities.

####  2.2.3. Limitations of Traditional Methods

HCI research has taken two approaches to investigating LMs’ generative
capabilities for interactive writing. The most traditional approach is
contextual inquiry, inviting writers to write with an LM and interviewing them
afterward (Calderwood et al., 2020; Clark et al., 2018; Gero and Chilton,
2019; Wu et al., 2020; Wu et al., 2021; Yang et al., 2019). For example,
Calderwood et al. (2020) interviewed four professional novelists after they
wrote fictional stories with GPT-2. This approach reveals rich insights about
how novelists interpreted the capabilities of LMs in specific contexts.
However, it is unclear to what extent the findings about GPT-2 would
generalize to other writing contexts, to other non-professional writers, to
future versions of GPT, or even to other configurations of the same model.
Similarly, tinkering with GPT-3 in the Playground (a text box where one can
submit a prompt to generate a completion) (OpenAI, 2020) is unlikely to cover
diverse contexts. In other words, contextual inquiry is more effective in
capturing the subjective interpretation of LMs’ generative capabilities than
covering diverse contexts.

An emerging approach to investigating LMs’ capabilities is to log interactions
and analyze them afterward (Roemmele and Gordon, 2018; Buschek et al., 2021).
For example, Roemmele and Gordon (2018) varied the degree of randomness of
suggestions generated by a recurrent neural network and tracked writers’ edits
to suggestions as a strategy for evaluation. Likewise, Buschek et al. (2021)
logged interactions between native or non-native English speakers and GPT-2
for email writing and analyzed their behavior patterns. Although this approach
may provide less rich insights than interviews, it can cover relatively
diverse contexts across tasks and writers, while allowing for a fine-grained
analysis of interactions. However, most previous work considered restricted
interaction settings (e.g. strict turn-taking (Clark and Smith, 2021)) and
adapted LMs and interfaces (e.g. find-tuned GPT-2 (Wang et al., 2021)) on
specific tasks (e.g. email writing (Buschek et al., 2021)), thereby making it
hard to generalize to other tasks and configurations of the same model.

###  2.3. Datasets in HCI

The challenges of understanding highly context-dependent and subjectively
interpreted capabilities are not unique to LMs (Yang et al., 2020a). In
understanding advances in technologies, a different approach has emerged:
developing interaction datasets and providing tools for data analyses (Cuadra
et al., 2021; Semmens et al., 2019; Wiegreffe and Marasović, 2021; Theodorou
et al., 2021; Yang et al., 2018). We distinguish datasets from logs, as logs
are usually byproducts of user studies and are not meant to be reused.
Nevertheless, this dataset approach shares strengths with the log analysis
approach in that it can cover diverse contexts, while supporting the
subjective interpretation of LMs’ capabilities in a different way.

  * •

Datasets can cover diverse contexts. For example, Theodorou et al. (2021)
published a dataset of photos taken by blind and low vision users, for
assessing how well different object recognition models can serve this user
population.

  * •

Datasets can account for the subjective interpretation of capabilities and
allow various interpretations. For example, Cuadra et al. (2021) provided a
video dataset capturing user interactions with a voice assistant without
defining which interactions are good. Instead, it opened up discussion around
how “good” interactions should be defined, and relatedly, what data-driven
interactions are desirable (e.g. What non-verbal cues should a voice assistant
detect? How should it respond accordingly?).

The HCI potential of the dataset approach informed this work, with the focus
on LMs’ generative capabilities for interactive writing.

###  2.4. Datasets in NLP

Datasets are central to evaluating LMs’ generative capabilities in NLP (van
der Lee et al., 2019; Celikyilmaz et al., 2020; Gehrmann et al., 2021;
Hendrycks et al., 2021; Kiela et al., 2021; Ruder, 2021). Typically, a dataset
is designed based on a task and collected at scale, containing text across
diverse topics from multiple sources and annotators. These datasets are often
assembled and combined into a benchmark in order to assess LMs’ capabilities
comprehensively. For example, Hendrycks et al. (2021) proposed a benchmark
consisting of 57 datasets including elementary mathematics, US history,
computer science, and law, to assess LMs’ world knowledge and problem-solving
ability. This benchmarking practice has been particularly helpful in assessing
recent LMs that can perform many tasks, since datasets are reusable when
evaluating many LMs, and are easily expandable to new tasks and contexts.
However, we argue that the underlying assumption of most datasets is the full-
automation of tasks rather than augmentation. In other words, they do not
consider interactive settings where users can guide and correct systems’
generated outputs, but rather expect LMs to generate correct answers alone. As
a result, these datasets tend to not capture the process of writing, but
rather focus on result. In this work, we aim to design reusable and expandable
datasets that capture the writing process.

Figure 2. We contrast the capabilities of GPT-3 with high randomness and low
randomness in creative and argumentative writing. To control randomness, we
varied two decoding parameters: temperature (T) and frequency penalty (FP).

Two images labelled as “Creative writing” and “Argumentative writing,” each of
which contains two lists of five suggestions generated by GPT-3 with high and
low randomness.

##  3\. Designing Datasets for HCI

We set out to investigate LMs’ generative capabilities for interactive
writing, making them more accessible for interaction design. Informed by the
preceding review of literature, we propose four desiderata for large
interaction datasets that can capture LMs’ generative capabilities:

  * •

Cover diverse contexts: Datasets should cover a wide range of contexts such as
writing tasks, writing prompts, and writers to account for the highly context-
dependent capabilities of LMs.

  * •

Support subjective interpretations: Datasets should refrain from imposing a
single label or metric. Instead, they should allow designers to extract
meaning from interactions and analyze them according to their own design
goals.

  * •

Capture processes, not just results: Datasets must capture the process of
writing that can provide designers a felt understanding of interaction.

  * •

Allow for possible reuse and expansion: Datasets need to be reusable and
expandable, given the fast advances of LMs and the resource-intensive process
of data collection.

##  4\. Designing CoAuthor

Applying these design principles, we created CoAuthor, a dataset for
understanding LMs’ generative capabilities for interactive writing. In what
follows, we first explain how we designed the dataset based on the four
desiderata, and then provide an overview of the dataset. In Section 5, we
provide example analyses of the dataset, demonstrating its utility to the HCI
community.

###  4.1. Dataset Design

Cover diverse contexts. CoAuthor contains interactions between writers and LMs
across multiple writing tasks, writing prompts, and writers.

  * •

Writing tasks: CoAuthor covers creative writing and argumentative writing,
which have distinct goals and require different sets of writing skills.
Creative writing, especially story writing, involves structural elements such
as character development, narrative, and plot, while infusing the structure
with imagination. Argumentative writing requires a writer to investigate a
topic by collecting, generating, evaluating evidence, and then establish a
position on the topic concisely (Lab, 2021).

  * •

Writing prompts: CoAuthor contains 20 writing prompts, brief passages of text
that provides a potential topic idea or starting point. We retrieved 10
creative writing prompts from the WritingPrompts subreddit (WritingPrompts,
2021), as these prompts have been successful in attracting writers and
providing writing inspiration. For argumentative writing, we used prompts from
The New York Times (Network, 2021) in order to provide an accessible, well-
balanced set of topics. Appendix B lists the prompts used.

  * •

Writers: We recruited 63 crowd workers (writers) from Amazon Mechanical Turk
to account for individuals with potentially different backgrounds and writing
styles.

Support subjective interpretations. CoAuthor supports multiple analytical
perspectives and goals by providing various measurements in three categories
that can be used to define good collaboration: writing outcome, writer
perception, and writer behavior.

  * •

Writing outcome: We consider a writing outcome to be the artifacts collected
at the end of a writing session (e.g. a full list of events and final texts),
with its associated measurements, such as time, length, total number of
queries, acceptance rate, and written-by-writers rate (i.e. the proportion of
the final text written by writers as opposed to the system).

  * •

Writer perception: We asked writers to fill out a survey to understand their
perception of LM’s generative capabilities as well as overall experience (e.g.
ownership and satisfaction) after each writing session.

  * •

Writer interaction: We measured how much interaction writers had with GPT-3 in
the writing process using the notions of equality and mutuality from Storch
(2002).

Category | Event | Event source | Key binding | Description
---|---|---|---|---
System | system-initialize | API | - | Initialize editor
Text | text-insert | {User, API} | (any key) | Insert text
(delta) | text-delete | User | delete | Delete text
Cursor | cursor-forward | {User, API} | {$\downarrow$, $\rightarrow$} | Move cursor forward
(range) | cursor-backward | User | {$\uparrow$, $\leftarrow$} | Move cursor backward
| cursor-select | User | shift + {$\downarrow$, $\rightarrow$, $\uparrow$, $\leftarrow$} | Select range of text
Suggestion | suggestion-get | User | tab | Request new suggestions
| suggestion-open | API | - | Show suggestions
| suggestion-reopen | User | shift + tab | Reopen previous suggestions
_While suggestions are shown_
| suggestion-up | User | $\uparrow$ | Navigate to suggestion above
| suggestion-down | User | $\downarrow$ | Navigate to suggestion below
| suggestion-select | User | enter | Select suggestion
| suggestion-close | {User, API} | esc or (any key) | Hide suggestions
Table 1. List of events. Text events have associated metadata “delta,” containing information on inserted or deleted text. Likewise, cursor events have associated metadata “range,” containing information on start and end indices of cursor selection. Category | Event block | Event sequence | Event source
---|---|---|---
System | init | system-initialize | API
Text | insert | (text-insert)+ | User
| delete | (text-delete)+ | User
Cursor | cursor | (cursor-forward — cursor-backward — cursor-select)+ | User
Suggestion | query |  suggestion-get (suggestion-close — cursor-forward)? suggestion-open |  User (API)? API
| reopen | suggestion-reopen | User
| navigate | (suggestion-up — suggestion-down)+ | User
| choose |  suggestion-select suggestion-close text-insert |  User API API
| dismiss | suggestion-close | User
Table 2. List of event blocks. Event blocks are deterministic, non-overlapping
abstraction of a sequence of events. Event sequence and source are represented
using regular expression syntax.

Capture processes, not just results. CoAuthor preserves details of rich
interactions as events and event blocks.

  * •

Events: An event can be inserting or deleting text, moving a cursor forward or
backward, getting suggestions from the system, or accepting or dismissing
suggestions. A list of all events are shown in Table 1 (e.g. text-insert(a)).
Formally, an event is a tuple of event name, timestamp, and snapshot of the
current editor, which is designed to preserve every detail about interactions.

  * •

Event blocks: Once recorded, events are abstracted into event blocks. An event
block is a deterministic, non-overlapping abstraction of a sequence of events
(e.g. text-insert(a) text-insert(b) $\rightarrow$ insert(ab)), which is
designed to be conducive for further processing and analysis. Table 2 lists
all event blocks.

Allow for possible reuse and expansion. We designed CoAuthor to be reusable
and easily expandable in the future.

  * •

Writing sessions: CoAuthor consists of a set of writing sessions. In each
writing session, a writer was presented with a prompt and given an instance of
a model with associated decoding parameters. Designers can potentially use a
subset of the writing sessions or collect more sessions based on their design
goals.

  * •

Language model: To generate suggestions, we used GPT-3 (Brown et al., 2020)
without any adaptation (e.g. fine-tuning). We only varied the randomness of
suggestions by changing its decoding parameters (similar to Roemmele and
Gordon (2018)) as shown in Figure 2. See Appendix A.2 for details.

  * •

System: We used a model- and task-agnostic user interface and interaction
design that resembles a text editor (Section 4.2). Future research can easily
expand this study and dataset to different writing tasks, prompts, and writers
using the same user interface and interaction design.

###  4.2. Data Collection Interface

Our user interface and interaction design was informed by related designs
(e.g. Write With Transformer and Buschek et al. (2021)). For alternative
interface and interaction designs, we refer readers to Clark et al. (2018) and
Coenen et al. (2021).

####  4.2.1. Interface

Our interface was a text editor initialized with a writing prompt and keyboard
shortcuts for functionality (Figure 3). The text editor was implemented using
Quill. The editor supported all typical interactions, such as typing,
selecting, editing, and deleting text, and cursor movement via keys and mouse.
At the bottom of the screen, a timer for minimum required writing time was
shown. After the time was over, the “Get verification code” button was
enabled, which writers clicked to finish writing sessions.

####  4.2.2. Interactions

When writers press the tab key, the system provided five suggestions in a
popup box below the cursor (Figure 3). While fetching the suggestions, the
icon next to the title (Writing with AI) spun to indicate that the system was
getting suggestions. Selecting suggestions in the list was possible via mouse
(point and click) or keyboard (arrow up and down to change selection, enter to
accept selected suggestion). These main commands were explained as part of the
study and shown in the keyboard shortcuts in the interface.

####  4.2.3. Suggestions

We consider a suggestion “accepted” if a writer selected it from the list. An
accepted suggestion is automatically appended at the end of the current text.
A group of suggestions is “dismissed” if a writer continues to type, clicks
outside of the suggestion popup box, or presses the escape key. The
suggestions are automatically hidden as they are dismissed.

###  4.3. Dataset Curation Procedure

####  4.3.1. Writers and prompts

We recruited crowd workers (writers) on Amazon Mechanical Turk. From $201$
writers who participated in our qualification round, we qualified $100$
writers. Among these writers, $63$ writers participated in the main round,
where $62$ writers were native English speakers and one was not. Table 4 and 5
in Appendix B list all prompts in the order they were shown to writers.
Writers could write about each prompt up to five times, or choose to skip
prompts if they wished not to write. We paid them $2.50 for each writing
session. See Appendix D for details.

####  4.3.2. Instructions

We instructed writers to spend at least ten minutes writing in response to a
given prompt. In the instructions, we specified that this was an open-ended
task, and the only two requirements were (1) to ensure the story or essay has
a clear ending or a clear stance and conclusion, and (2) to collaborate with
the system to write a story or an essay for ten minutes. We included self-
evaluation of two requirements after each writing session in order to remind
writers of the requirements and control the quality of final texts. See Figure
11 in Appendix E for full instructions.

####  4.3.3. Survey

We asked writers to fill out a survey after each writing session to contrast
the capabilities of LMs across different prompts and qualities associated with
the system. The survey questions consisted of five sections: writer
information (native vs. non-native English speaker), assessment of the known
benefits of collaborative writing (fluency, pooling of ideas, and enhanced
quality), perceived capabilities of LMs, perceived limitations of LMs, and
overall experiences (ownership, satisfaction, and willingness to reuse). See
Appendix C for the full list of questions.

Figure 3. Interface used for data collection of CoAuthor. Our interface was a
text editor in which writers press the tab key to get suggestions from the
system whenever desired.

Screenshot of the interface used for data collection. The interface consists
of a header with the title “Writing with AI,” a small floating box for
keyboard shortcuts, a text editor with an example prompt and five suggestions
in a popup box, and a footer containing a timer and the “Get verification
code” button.

###  4.4. Dataset Overview

| Overall | Writing sessions
---|---|---
| Prompts | Writers | Sessions | Time | Length | Queries | Acceptance rate | Written by writers
|  |  |  | (minutes) | (words) |  | (%) | (%)
Creative | 10 | 58 | 830 | 11.6 | 446 | 12.8 | 75.7 | 72.7
Argumentative | 10 | 49 | 615 | 10.6 | 380 | 10.3 | 67.6 | 72.5
Combined | 20 | 63 | 1445 | 11.2 | 418 | 11.8 | 72.3 | 72.6
Table 3. Overall statistics of CoAuthor. The dataset consists of a set of
writing sessions in the two types of writing: Creative and argumentative
writing. For each type, ten writing prompts were provided, from which writers
could write up to five times per prompt. The dataset contains 1445 writing
sessions written by 63 writers. On average, each writing session is 418 words
long, contains 11.8 queries to the system, has acceptance rate of 72.3% (how
often writers accepted suggestions from GPT-3), and results in 72.6% of final
texts written by writers (as opposed to GPT-3).

Table 3 shows overall statistics about CoAuthor. The dataset contains $830$
stories written by $58$ writers for creative writing and $615$ essays written
by $49$ writers for argumentative writing. On average, each writing session
results in 418 words of text, contains 11.8 queries to the system, has
acceptance rate of $72.3$%, and $72.6$% of text is written by writers.

##  5\. Demonstrating Uses of CoAuthor

This section demonstrates the usefulness of CoAuthor for revealing GPT-3’s
generative capabilities in assisting creative and argumentative writing.
Specifically, it allows designers to explore the generative capabilities of
LMs holistically and to reason about its contribution as a writing
“collaborator” under various definitions of good collaboration.

###  5.1. GPT-3’s Generative Capabilities

To gain a holistic understanding of GPT-3’s generative capabilities for
interactive writing, we looked at three aspects of capabilities using CoAuthor
and compared our findings to existing hypotheses. First, we study language
capabilities (ability to generate fluent text). In Section 5.1.1, we show that
the sentences generated by GPT-3 had less spelling and grammar errors than the
sentences written by writers in CoAuthor. Second, we focus on ideation
capabilities (ability to generate new ideas). In Section 5.1.2, we present
evidence that GPT-3 is capable of providing new ideas to writers, influencing
their subsequent writing. Lastly, we investigate collaboration capabilities
(ability to work jointly with writers). In Section 5.1.3, we demonstrate that
the amount of collaboration between writers and GPT-3 varied significantly
across writers, but less depended on writing prompts and the randomness of
suggestions.

####  5.1.1. Language Capabilities: Ability to Generate Fluent Text

To study language capabilities of GPT-3, we compared the grammaticality and
vocabulary diversity of sentences written by writers alone, GPT-3 alone, and
writers and GPT-3 together in CoAuthor. For simplicity, we considered the
sentences from final texts, as opposed to sentences during the writing process
that might have been edited or deleted later on and do not appear in final
texts.

(a) Grammaticality (b) Vocabulary diversity

Figure 4. Sentences written by both writers and GPT-3 had fewer spelling and
grammatical errors (a) and contained more diverse vocabulary (b) compared to
sentences written by writers alone and GPT-3 alone.

(a) Bar charts showing the number of errors per word on the y-axis and authors
(writer, GPT-3, and both) on the x-axis. Three bars are shown. The text
written by writers had more spelling and grammar errors compared to
GPT-3-generated sentences, with a reasonably large drop from 0.037 to 0.033
with the standard error of measurement of 0.001. The text written by both had
least spelling and grammar errors on average (0.032). (b) Bar charts showing
the Distinct-2 score on the y-axis and authors on the X axis. Three bars are
shown. The text written by both had contained most diverse vocabulary, with a
reasonably large jump from 0.884 (writer) and 0.876 (GPT-3) to 0.923 (both)
with the standard error of measurement of 0.001.

How grammatical is GPT-3’s writing? Figure 4 (a) shows that sentences written
by writers had more spelling and grammar errors compared to GPT-3-generated
sentences in final texts. We measured grammaticality by averaging over the
number of spelling and grammar errors across sentences using LanguageTool.
Overall, the number of errors per word (averaged across all sentences in
creative and argumentative writing) was $0.037\pm 0.001$ for writers,
$0.033\pm 0.001$ for GPT-3, and $0.032\pm 0.001$ for both (the number next to
the average indicates the standard error of measurement). This matches with
Dou et al. (2021)’s finding that sentences written by writers tend to have
more grammar and usage errors compared to those written by GPT-3.

How diverse is GPT-3’s vocabulary? Figure 4 (b) shows that sentences written
by both writers and GPT-3 contained more diverse vocabulary compared to the
sentences written by writers alone and GPT-3 alone. The vocabulary diversity
was measured by counting the number of unique bigrams scaled by total number
of generated words (distinct-2) (Li et al., 2016). Overall, the distinct-2
score (averaged across all sentences in creative and argumentative writing)
was $0.884\pm 0.001$ for writers, $0.876\pm 0.001$ for GPT-3, and $0.923\pm
0.001$ for both. The result may imply that the use of suggestions from GPT-3
encouraged writers to use more diverse vocabulary.

This matches with the previous findings about machine-generated text being
less diverse than human-authored counterparts (Hashimoto et al., 2019). Note
that the sentences in final texts only include suggestions from GPT-3 which
were accepted by writers. In other words, dismissed suggestions generated by
GPT-3 (which may contain much less diverse vocabulary) were not included in
the previous analysis. When we computed the distinct-2 score on all
suggestions generated by GPT-3, the score was even lower ($0.868\pm 0.001$),
further confirming the previous findings.

(a) Creative writing (b) Argumentative writing

Figure 5. For both creative (a) and argumentative (b) writing, the number of
spelling and grammar errors per word (y-axis) in GPT-3-generated sentences
vary across writing prompts (x-axis).

Bar charts showing the number of errors per word on the y-axis and prompts on
the x-axis. Ten bars are shown for creative writing (a) and argumentative
writing (b), representing ten prompts used in each writing task. The number of
errors per word varied significantly across prompts, with the largest gap
between two prompts being near 0.03.

Figure 6. Example of a story in which a writer accepted a suggestion from
GPT-3 with a new named entity “Dr. John” and used the entity in the subsequent
writing. The prompt is shown in black, sentences written by the writer in
brown, and sentences written by GPT-3 in blue.

Example of a story in which a writer accepted a suggestion from GPT-3 with a
new named entity “Dr. John” and used the entity in the subsequent writing. Two
instances of reuse are shown in the example story and the rest is omitted.

Do prompts influence GPT-3’s grammaticality? The grammaticality of
GPT-3-generated text varies across prompts (Figure 5b). This observation
matches with recent findings that LMs have high variance performance based on
the choice of prompts (Liu et al., 2021). In survey responses, we find varying
degree of writers’ reactions to the suggestions they received. Positive
responses include “Didn’t notice any grammar errors or anything” and “I am not
a woman, or who reads Romance novels, but the AI apparently is! It did a grand
job of writing this time; beside grammar and formatting, there is nothing I
saw that could be improved.” Negative ones include “Some of the AI suggestions
had some typos, so they were a little grammatically incorrect. For example,
the AI suggested a sentence with the word “imminent” being spelled “emminent,”
which is not correct. I used the sentence, but had to correct the misspelling.
So the grammar could be improved slightly.”

####  5.1.2. Ideation Capabilities: Ability to Generate New Ideas

Some existing work uses the amount of text written by the system or the number
of edits made by writers to estimate the system’s “usefulness” or degree of
contribution (Roemmele and Gordon, 2018; Clark and Smith, 2021). However, this
notion of usefulness is limited, as writers may find inspiration in
suggestions (or portions thereof) or even dismissed suggestions. To find
evidence that LMs can provide new ideas, we identified GPT-3-generated
sentences with new named entities (a real-world object, such as a person and
location, that can be denoted with a proper name) that did not appear in their
previous contexts. Then, we checked whether they were reused by writers in
subsequent writing.

Figure 6 shows an example story from CoAuthor where a new named entity was
introduced by GPT-3 and subsequently reused by a writer. We used Stanza (Qi et
al., 2020) to identify named entities and exact match to check whether they
were reused in subsequent writing. Note that this is likely to underestimate
GPT-3’s contributions, since new ideas are not always expressed as named
entities and names may appear in a different form in the later text (e.g.
pronouns).

How often do suggestions contain new named entities? $13$% and $7$% of
accepted suggestions contained new named entities in creative and
argumentative writing, respectively (Figure 7). We suspect that this
difference could be due to the different writing types (e.g. stories are more
likely to need new names and locations) and the different sets of decoding
parameters (temperature and frequency penalty as shown in Figure 2).

Figure 7. In creative writing, 13% of suggestions (generated by GPT-3)
contained new named entities, among which 20% were reused by writers in
subsequent writing.

Pie graphs showing the percentage of GPT-3-generated suggestions that
contained new named entities (left) and the percentage of new named entities
reused by writers in subsequent writing (right). 13% of GPT-3-generated
suggestions contained new named entities, among which 20% were reused by
writers in subsequent writing.

How often are the new named entities reused by writers? Among the new named
entities proposed by GPT-3, $20$% and $14$% of them were reused by writers in
subsequent writing in creative and argumentative writing, respectively (Figure
7). Survey responses show that some writers liked and found the suggestions
with new named entities helpful (e.g. “I especially found the names helpful. I
was actually trying to think of a stereotypical rich jock name, and the AI
provided me with Chadwick. Perfect!”, “I found the suggestions that introduced
new characters to be most helpful. They really helped push the story along
nicely.”).

Does the randomness of GPT-3 influence its likelihood of generating new named
entities? The GPT-3 instance with high randomness was more likely to generate
sentences with new named entities compared to the one with low randomness,
while the likelihoods of the entities being reused by writers were similar. In
creative writing, accepted suggestions from GPT-3 with high randomness
contained new named entities more often ($15\pm 1$%), compared to the ones
from GPT-3 with low randomness ($10\pm 0$%). However, the likelihoods of new
named entities being reused by writers were similar for both high randomness
($19\pm 2$%) and low randomness ($22\pm 2$%). Similarly, in argumentative
writing, accepted suggestions from GPT-3 with high randomness contained new
named entities more often ($9\pm 1$%), compared to the ones from GPT-3 with
low randomness ($6\pm 0$%). Yet, the likelihoods of new named entities being
reused by writers were similar for both high randomness ($13\pm 2$%) and low
randomness ($15\pm 3$%).

####  5.1.3. Collaboration Capabilities: Ability to Work Jointly

To investigate the extent that writers interact with GPT-3 in the writing
process, we adapted the notions of equality and mutuality from Storch (2002).
We redefined equality as how evenly a writer and GPT-3 distributed turns (e.g.
the degree of deviation from even division of work) and mutuality as the level
of interaction a writer has with GPT-3 (e.g. querying multiple times, choosing
suggestions, reopening suggestions, and navigating through suggestions).
Concretely, we computed equality and mutuality scores for a writing session by
counting the number of event blocks as follows. Let

$\mathcal{H}=\\{\text{{{insert}}}\\},\;\mathcal{M}=\\{\text{{{choose}}}\\}$

be the sets representing human-generated event blocks and machine-generated
event blocks, respectively. Also, let

$\displaystyle\mathcal{I}$
$\displaystyle=\\{\text{{{insert}}},\text{{{choose}}},\text{{{reopen}}},\text{{{navigate}}}\\},$
$\displaystyle\mathcal{A}$
$\displaystyle=\\{\text{{{dismiss}}},\text{{{insert}}},\text{{{delete}}}\\}$

denote the sets of event blocks corresponding to human-machine interactions
and event blocks corresponding to writing alone, respectively. Given a set of
events $\\{e_{i}\\}$, we define:

$\text{equality}=1-\bigg{\rvert}\frac{\sum_{i}[e_{i}\in\mathcal{H}]-\sum_{i}[e_{i}\in\mathcal{M}]}{\sum_{i}[e_{i}\in\mathcal{H}]+\sum_{i}[e_{i}\in\mathcal{M}]}\bigg{\rvert},$

where $[P]=1$ if $P$ is true and 0 if not. Moreover, define:

$\text{mutuality}=\frac{\sum_{i}[e_{i}\in\mathcal{I}]}{\sum_{i}[e_{i}\in\mathcal{I}]+\sum_{i}[e_{i}\in\mathcal{A}]}.$

An equality score of $1$ means perfect division of turns for writing and $0$
means that there was no turn change (i.e. it was written entirely by a writer
or GPT-3). For mutuality, score $1$ means writers interacted with GPT-3 the
entire time (without writing on their own as a result) and $0$ means writers
never interacted with GPT-3. Note that these scores are not based on final
texts, but event blocks. This accounts for text written (by either writers or
GPT-3) during the writing session, that may not be present in final texts.

Do writers and prompts influence collaboration? Both collaboration equality
and mutuality varied greatly depending on writers, but less on prompts. Figure
8b shows varying degrees of equality and mutuality scores across writers: for
instance, Writer #1 scored around 0.45 and 0.75 for equality and mutuality,
whereas Writer #2 scored below 0.1 and 0.2 (the scores are averaged over all
writing sessions). On the other hand, both equality and mutuality scores did
not fluctuate as much across writing prompts, as shown in Figure 9b. This
result may indicate that differences between writers are more likely to
influence the degree of collaboration rather than those of writing prompts
(see Table 4 and 5 in Appendix B for the full list of prompts).

(a) Equality (b) Mutuality

Figure 8.  Equality (a) and mutuality (b) (y-axis) vary across writers
(x-axis) greatly. Also, some writers had more equal and mutual collaboration
with GPT-3 with high randomness (dark blue), whereas others had such
collaboration with GPT-3 with low randomness (light blue).

Bar charts showing the equality (a) and mutuality (b) scores on the y-axis and
writers on the x-axis. Twenty bars are shown for each chart, representing the
averaged scores per writer under GPT-3 with low randomness and high
randomness. It shows varying degrees of equality and mutuality scores across
writers. For instance, Writer #1 scored around 0.45 and 0.75 for equality and
mutuality, whereas Writer #2 scored below 0.1 and 0.2.

(a) Creative writing

(b) Argumentative writing

Figure 9.  Equality and mutuality (y-axis) and vary across prompts (x-axis) in
creative (a) and argumentative writing (b), but to the less extent than across
writers.

Bar charts showing the equality and mutuality scores on the y-axis and prompts
on the x-axis for creative writing (a) and argumentative writing (b). Twenty
bars are shown for each chart, representing the averaged scores per prompt
under GPT-3 with low randomness and high randomness. It shows relatively less
varying degrees of equality and mutuality scores across prompts compared to
those across writers.

Does the randomness of GPT-3 influence collaboration? Writing sessions with
GPT-3 with high randomness received slightly higher equality and mutuality
scores in argumentative writing, while the difference was not statistically
significant in creative writing. In argumentative writing, the sessions with
GPT-3 with high randomness resulted in equality of $0.29\pm 0.01$ and
mutuality of $0.47\pm 0.01$, whereas GPT-3 with low randomness resulted in
$0.22\pm 0.01$ and $0.38\pm 0.01$ on average (Figure 9b (b)). When aggregated
by prompts, 9 out of 10 prompts (90%) had higher equality and mutuality scores
with GPT-3 with high randomness. On the other hand, in creative writing,
writing sessions with GPT-3 with high randomness resulted in $0.30\pm 0.01$
for equality and $0.48\pm 0.01$ for mutuality, whereas the sessions with GPT-3
with low randomness resulted in $0.29\pm 0.01$ and $0.49\pm 0.01$,
respectively (Figure 9b (a)). When aggregated by prompts, 5 out of 10 prompts
(50%) and 8 out of 10 prompts (80%) had higher equality and mutuality scores
with GPT-3 with low randomness.

On a granular level, writers have different preferences over suggestions
generated by GPT-3 with high randomness and low randomness. Figure 8b shows
that some writers (e.g. Writer #3 and #5) had more equal and mutual
collaboration when they interacted with GPT-3 with high randomness, whereas
others (e.g. Writer #7) had this type of collaboration with GPT-3 with low
randomness. This could be due to writers having different purposes of
collaborating with the system that made one more preferable than the other in
two writing types. For example, in creative writing, some writers used the
system to advance plots (62.9%), whereas some used it to add details to
stories (51.4%) according to the survey’s multiple choice question. In this
example, the former group of writers might have preferred suggestions from
GPT-3 with high randomness, as they tend to be more diverse. On the other
hand, the latter might have preferred suggestions from GPT-3 with low
randomness, as they tend to be more grammatical and coherent (Roemmele and
Gordon, 2018).

###  5.2. Various Definitions of Good Collaboration

In addition to addressing broad questions about GPT-3’s generative
capabilities, CoAuthor can also help researchers reason about GPT-3’s
contribution as a writing “collaborator” under various definitions of good
collaboration. Here, we consider two examples definitions (productivity and
ownership) and show how CoAuthor may provide preliminary evidence for
formulating hypotheses regarding interaction design. Note that we use
simplified definitions and generate hypotheses based on correlations. To
validate the hypotheses, further experiments with interventions are necessary.

####  5.2.1. Increasing writers’ productivity.

Consider a design scenario where a designer considers how to design a
GPT-3-powered auto-complete system to increase writers’ productivity. CoAuthor
reveals a number of factors that are positively correlated with the amount of
text the writers and GPT-3 end up producing. These factors include the time
writers spent writing, the number of queries, and the number of accepted
suggestions. The correlation was much stronger for the number of queries and
accepted suggestions (0.42 and 0.48) compared to the time writers spent
writing (0.29). This observation suggests that having suggestions from GPT-3
has the potential to increase writers’ productivity. The designer can further
investigate individual instances where this correlation was strong or weak and
make more informed decisions, before deploying GPT-3.

####  5.2.2. Increasing writers’ feeling of ownership.

Consider another scenario where a designer wants to increase writers’ feeling
of ownership over their GPT-3-assisted writing. In this case, the designer
could consider devising ways to keep the fraction of text written by writers
to text written by GPT-3 relatively high. We observe correlation between the
ownership writers have over final texts and the fraction of text written by
writers. For ownership scores (rated as a 5-point Likert scale) and the
fraction of text written by writers, the Pearson correlation coefficient was
$0.3$ in both creative and argumentative writing, whereas it was $0.1$ and
$0.0$ for satisfaction scores. This result may imply that the more writers get
suggestions from GPT-3, the less they write and the less they feel ownership
over final texts.

On the other hand, encouraging writers to make more edits (on their own
writing and GPT-3-generated sentences) may not be as effective in increasing
writers’ feeling of ownership. In human-human collaborative writing, Birnholtz
et al. (2013) suggested that the quantity of collaboration (e.g. number of
comments and edits) may affect writers’ perceived ownership of final texts and
attractiveness of the group task. However, we did not observe meaningful
correlation between the amount of edits by writers and their ownership or
satisfaction scores in CoAuthor. We approximated the number of edits by
counting the number of delete and cursor event blocks in each writing session.
For ownership score, the Pearson correlation coefficient was $0.1$ in both
creative and argumentative writing. For satisfaction score, the coefficient
was $0.0$ and $-0.1$ in creative and argumentative writing.

##  6\. Discussion

Excitement about LMs’ promises over their perils are often rooted in
observations of their particular behaviors in very restricted interaction
settings. These observations are rarely cross-referenced with the literature
in NLP that attempts to examine LMs’ generative capabilities holistically. In
this section, we aim to bring together these discussions, which often occur in
isolation. We first discuss the role of datasets as boundary objects between
the HCI and NLP communities. Then, we describe potential use cases of CoAuthor
in HCI and NLP research, exemplifying the potential of CoAuthor serving as a
boundary object.

###  6.1. Datasets as Boundary Objects

We argue that datasets have the potential to serve as boundary objects between
the HCI and NLP communities. For example, datasets in NLP can provide
resources to help HCI researchers reason about LMs’ generative capabilities in
a holistic manner. HCI researchers can provide analytical tools for NLP
researchers to better investigate LMs’s capabilities interactive settings.
Moreover, large interaction datasets in HCI can embed human-centered values
and practices, and further incorporate them into technical advances, when used
to train or evaluate LMs. By having datasets as boundary objects, the HCI and
NLP communities can easily communicate results and have shared understanding
of LMs’ generative capabilities.

###  6.2. Potential Use Cases of CoAuthor

####  6.2.1. Formulate Hypotheses

HCI researchers can use CoAuthor to formulate hypotheses via replay enactment
(Holstein et al., 2020). Concretely, researchers can replay writing sessions
in CoAuthor, which materializes the dynamics of interactions between writers
and GPT-3 and makes complex system behavior more tangible. Through replay,
researchers may discover, for example, that some writers prefer to get
suggestions from GPT-3 in the beginning, whereas others prefer to do so
throughout the writing process. Researchers with an eye towards building
personalized writing assistants may notice similarities and differences across
writers, or specific needs for certain writers.

####  6.2.2. Assess the Plausibility of Hypotheses

CoAuthor can serve as a starting point to assess the plausibility of
hypotheses. For instance, to investigate how the style, voice, or tone of a
writer or GPT-3 influences that of the other over time (i.e. linguistic
accommodation), researchers can first check the existence such phenomena in
CoAuthor as supporting evidence to warrant further investigation (e.g.
recruiting a specific set of participants for initial interview). Similarly,
if the above hypothesis is true, researchers can examine whether the influence
is uni-directional (e.g. from writer to GPT-3) or bi-directional (e.g. both
from writer to GPT-3 and from GPT-3 to writer) by analyzing events and
associated timestamps within each writing session. How users adapt to systems
over time is of interest for not only HCI researchers but also NLP researchers
(Wang et al., 2016, 2017).

####  6.2.3. Train and Evaluate Language Models

NLP researchers can train and evaluate LMs on CoAuthor to better support
interactive writing. One can expect that a LM fine-tuned on accepted
suggestions may generate suggestions that are more desirable by writers,
compared to the same LM without any fine-tuning. Most current LMs are trained
and evaluated on datasets that do not consider interactive settings. On the
other hand, our dataset considers an interactive setting, which seamlessly
embodies in-situ generation and evaluation. Additional information in the
dataset (e.g. when writers asked for suggestions, which suggestions they
accepted, dismissed, and modified, and how they perceived the capabilities of
GPT-3) can provide supervision signals for LMs to learn desirable behaviors.

##  7\. Conclusion

In this work, we identified a critical need for understanding LMs’ generative
capabilities for interaction design. We argued that curating and analyzing
large interaction datasets is one viable approach, as it can cover diverse
interaction contexts and support various interpretations of good
collaboration. Exemplifying this approach, we created CoAuthor, a dataset
containing rich interactions between 63 writers and four instances of GPT-3
across 1445 writing sessions. We demonstrated the feasibility of this approach
and discussed insights designers can draw from the dataset. We encourage
fellow researchers to use, analyze, and extend CoAuthor, based on their
respective design goals and research perspectives.

###### Acknowledgements.

We thank Chris Donahue and Daniel Jiang as well as anonymous reviewers for
valuable feedback. We gratefully acknowledge the support of a PECASE award.

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##  Appendix A System settings

###  A.1. Server

Our server is written in Python using Flask, and is used by the frontend both
to request suggestions and to save events in a writing session. We deployed
the system on our institution’s infrastructure.

###  A.2. Decoding parameters

We used the following decoding parameters for GPT-3 (Brown et al., 2020) to
generate suggestions.

  * •

Engine: davinci

  * •

Response length (word piece): 30

  * •

Temperature: 0.2, 0.3, 0.75, 0.9

  * •

Top P: 1

  * •

Frequency penalty: 0, 0.5, 1

  * •

Presence penalty: 0

  * •

Best of: 1

###  A.3. Suggestions

Once GPT-3 generated an output given a prompt, it was parsed as a list of
sentences using Stanza (Qi et al., 2020), and only the first sentence was used
as a suggestion for readability. In addition, despite querying GPT-3 five
times, we oftentimes ended up with a fewer suggestions due to suggestions
containing swear words, duplicate strings, and empty strings, which were
filtered out and not shown to writers. For more rigorous filtering, we
recommend using toxicity detection to exclude inappropriate suggestions.

##  Appendix B Writing Prompts

Prompt code |  Prompt text (Source URL)
---|---
shapeshifter |  A woman has been dating guy after guy, but it never seems to work out. She’s unaware that she’s actually been dating the same guy over and over; a shapeshifter who’s fallen for her, and is certain he’s going to get it right this time.
|
(https://www.reddit.com/r/WritingPrompts/comments/7xihva/wp_a_woman_has_been_dating_guy_after_guy_but_it/)
reincarnation |  When you die, you appear in a cinema with a number of other people who look like you. You find out that they are your previous reincarnations, and soon you all begin watching your next life on the big screen.
|
(https://www.reddit.com/r/WritingPrompts/comments/7ezd5t/wp_when_you_die_you_appear_in_a_cinema_with_a/)
mana |  Humans once wielded formidable magical power. But with over 7 billion of us on the planet now, Mana has spread far too thinly to have any effect. When hostile aliens reduce humanity to a mere fraction, the survivors discover an old power has begun to reawaken once again.
|
(https://www.reddit.com/r/WritingPrompts/comments/7i3bs6/wp_humans_once_wielded_formidable_magical_power/)
obama |  You’re Barack Obama. 4 years into your retirement, you awake to find a letter with no return address on your bedside table. It reads “I hope you’ve had a chance to relax Barack… but pack your bags and call the number below. It’s time to start the real job.” Signed simply, “JFK.”
|
(https://www.reddit.com/r/WritingPrompts/comments/6b3rmg/wp_youre_barack_obama_4_months_into_your/)
pig |  Once upon a time there was an old mother pig who had one hundred little pigs and not enough food to feed them. So when they were old enough, she sent them out into the world to seek their fortunes. You know the story about the first three little pigs. This is a story about the 92nd little pig. The 92nd little pig built a house out of depleted uranium. And the wolf was like, “dude.”
|
(https://www.reddit.com/r/WritingPrompts/comments/hytfcd/wp_then_the_92nd_little_pig_built_a_house_out_of/)
mattdamon |  An alien has kidnapped Matt Damon, not knowing what lengths humanity goes through to retrieve him whenever he goes missing.
|
(https://www.reddit.com/r/WritingPrompts/comments/8p3ora/wp_an_alien_has_kidnapped_matt_damon_not_knowing/)
sideeffect |  When you’re 28, science discovers a drug that stops all effects of aging, creating immortality. Your government decides to give the drug to all citizens under 26, but you and the rest of the “Lost Generations” are deemed too high-risk. When you’re 85, the side effects are finally discovered.
|
(https://www.reddit.com/r/WritingPrompts/comments/8on59a/wp_when_youre_28_science_discovers_a_drug_that/)
bee |  Your entire life, you’ve been told you’re deathly allergic to bees. You’ve always had people protecting you from them, be it your mother or a hired hand. Today, one slips through and lands on your shoulder. You hear a tiny voice say “Your Majesty, what are your orders?”
|
(https://www.reddit.com/r/WritingPrompts/comments/88p6rp/wp_your_entire_life_youve_been_told_youre_deathly/)
dad |  All of the “#1 Dad” mugs in the world change to show the actual ranking of Dads suddenly.
|
(https://www.reddit.com/r/WritingPrompts/comments/6gl289/wp_all_of_the_1_dad_mugs_in_the_world_change_to/)
Table 4. For creative writing, we retrieved prompts from the WritingPrompts subreddit (WritingPrompts, 2021) and used them with minor modifications. Prompt code |  Prompt text (Source URL)
---|---
screen |  How Worried Should We Be About Screen Time During the Pandemic? The coronavirus pandemic ended the screen time debate: Screens won. We all now find ourselves on our screens for school, for work and for connecting with family and friends during this time of social distancing and increased isolation. But should we be worried about this excessive screen use right now? Or should we finally get over it and embrace the benefits of our digital devices?
|  (https://www.nytimes.com/2021/01/22/learning/how-worried-should-we-be-
about-screen-time-during-the-pandemic.html)
dating |  How Do You Think Technology Affects Dating? Have you had any experience with dating? Have you ever used dating apps? If so, what has it been like for you? If not, why not? How do you think technology — like apps, Netflix, social media and texting — affects dating and relationships? In your opinion, does it improve or worsen romantic interactions? How so?
|  (https://www.nytimes.com/2018/02/21/learning/how-do-you-think-technology-
affects-dating.html)
pads |  Should Schools Provide Free Pads and Tampons? Have you ever experienced period shaming, or “period poverty”? Should schools step in to help? Should schools be required to provide free pads and tampons to students? How are pads and tampons similar to toilet paper, soap, Band-Aids and other products that are already provided in schools? How are they different?
|  (https://www.nytimes.com/2020/11/18/learning/should-schools-provide-free-
pads-and-tampons.html)
school |  What Are the Most Important Things Students Should Learn in School? In your opinion, what are the most important things students should learn in school? What is the most important thing you have learned in school? How has this knowledge affected your life? How do you think it will help your success in the future?
|  (https://www.nytimes.com/2019/02/21/learning/what-are-the-most-important-
things-students-should-learn-in-school.html)
stereotype |  What Stereotypical Characters Make You Cringe? What stereotypical characters in books, movies or television shows make you cringe and why? Would you ever not watch or read something because of its offensive portrayal of someone?
|  (https://www.nytimes.com/2017/11/16/learning/what-stereotypical-characters-
make-you-cringe.html)
audiobook |  Is Listening to a Book Just as Good as Reading It? Do you listen to audiobooks? What are the benefits, in your opinion, of listening instead of reading? Are there advantages to reading that cannot be gained by listening? Which method do you prefer? Why?
|  (https://www.nytimes.com/2018/12/12/learning/is-listening-to-a-book-just-
as-good-as-reading-it.html)
athletes |  Should College Athletes Be Paid? Do you think college athletes should be paid? Or is a college scholarship and other non-monetary perks like the opportunity to play in front of cheering fans enough? […] What possible difficulties or downsides might there be in providing monetary compensation to players?
|  (https://www.nytimes.com/2019/02/26/learning/should-college-athletes-be-
paid.html)
extremesports |  Is It Selfish to Pursue Risky Sports Like Extreme Mountain Climbing? Some sports, like extreme mountain climbing, are dangerous. Since there are varying degrees of risk in most, if not all, sports (such as the possibility of concussions, broken bones and even death), how does one decide where the line might be drawn between what is reasonable and what is not? Are some sports simply too dangerous to be called a sport?
|  (https://www.nytimes.com/2019/04/29/learning/is-it-selfish-to-pursue-risky-
sports-like-extreme-mountain-climbing.html)
animal |  Is It Wrong to Focus on Animal Welfare When Humans Are Suffering? Would you be surprised to hear that a study found that research subjects were more upset by stories of a dog beaten by a baseball bat than of an adult similarly beaten? Or that other researchers found that if forced to choose, 40 percent of people would save their pet dog over a foreign tourist. Why do you think many people are more empathetic toward the suffering of animals than that of people? In your opinion, is it wrong to focus on animal welfare when humans are suffering? Why do you think so?
|  (https://www.nytimes.com/2018/04/11/learning/is-it-wrong-to-focus-on-
animal-welfare-when-humans-are-suffering.html)
news |  Are We Being Bad Citizens If We Don’t Keep Up With the News? In your opinion, are we being bad citizens if we don’t keep up with the news? Do you think all people have some responsibility to know what is going on in the world? Does engaging with current events actually do anything at all? Why do you think the way you do?
|  (https://www.nytimes.com/2018/03/20/learning/are-we-being-bad-citizens-if-
we-dont-keep-up-with-the-news.html)
Table 5. For argumentative writing, we retrieved prompts from The New York
Times (Network, 2021) and used them with minor modifications. At the end of
each prompt, we added “In my opinion,” to give GPT-3 a clear signal to start
responding to the prompt (as opposed to generating the continuation of the
prompt).

Table 4 and 5 show ten prompts used in creative writing and argumentative
writing, respectively. The prompts were retrieved from the WritingPrompts
subreddit (WritingPrompts, 2021) and The New York Times (Network, 2021) with
minor modifications. Prompt codes were assigned by authors to easily refer to
specific prompts in the paper.

(a) Creative writing (b) Argumentative writing

Figure 10. For both (a) creative and (b) argumentative writing, the number of
times a writer (y-axis) chose a writing prompt (x-axis). Each writer could
continue working on the same prompt up to five times.

Heatmap showing prompts on the x-axis and writers on the y-axis. Each cell has
a number between 0 and 5, representing the number of times a writer chose to
work on a writing prompt. Some writers worked on each prompt roughly once,
whereas some writers worked on each prompt five times, which was the maximum
amount they could continue working on the same prompt.

Recall that writers could write about each prompt up to five times or choose
to skip prompts if they wished not to write. Figure 10b shows the number of
writing sessions per prompt and the top 20 writers with most participation. We
observe that some writers chose to write about each prompt repeatedly (e.g.
A2W, A3O, and A34 in Figure 10b (a)), whereas others wrote about each prompt
one or twice and moved onto next prompt (e.g. A3O and A11 in Figure 10b (a)).
Also, writers sometimes skipped certain prompts completely (e.g. A1Q chose not
to write about dating (da) in Figure 10b (b)).

##  Appendix C Survey Questions

Our survey consisted of five sections: writer information, benefits of
collaborative writing, perceived capabilities of LMs, perceived limitations of
LMs, and overall experiences. For writer information, we asked writers whether
English is their first language, accounting for the different capabilities of
LMs on native and non-native English speakers (Buschek et al., 2021) (Is
English your first language?). For benefits of collaborative writing, we
wanted to understand whether human-LM collaborative writing has the known
benefits of human-human collaborative writing, such as increase in fluency
(Bloch, 2007), pooling of knowledge and ideas (Donato, 1994), and enhanced
writing quality (Storch, 2005). To this end, we asked writers whether the
suggestions they received contributed to the fluency of the resultant text,
whether the suggestions helped them come up with new ideas, and whether they
felt like that they would have written a better essay if they wrote the essay
alone in 7-point Likert scale. For perceived capabilities of LMs, we wanted to
understand capabilities of LMs perceived by writers. Specifically, we
considered the notion of competence (having expert knowledge and ability to
perform a task successfully) (Mayer et al., 1995) and asked writers whether
they think the system was competent in writing, whether the system was capable
of writing creative stories or persuasive essays, and whether the system
understood what they were trying to write. For perceived limitations of LMs,
we asked writers which aspects of the suggestions (that they received during
each writing session) can be improved and ask them to provide specific
examples they have. For overall experience, we included common questions asked
in NLP papers. Then, we checked whether some of our hypotheses are
meaningfully reflected and observable through these questions. We asked
writers about ease of writing (It was easy to write with the system),
satisfaction (I am satisfied with the story/essay I wrote.), confidence (I am
confident in my ability to write a story/essay with the help of the system.),
ownership (I feel like the story/essay is mine.), and willingness to reuse (If
the system is available for free, I would reuse the system.).

##  Appendix D Qualification Round

In the qualification round, the following conditions were used to allow
experienced crowd workers (writers) to participate in our qualification round:

  * •

HIT Approval Rate (%) for all Requesters’ HITs is greater than $97$

  * •

Location is the United States

  * •

Number of HITs Approved is greater than $10000$.

We specified two requirements for passing the qualification in the
instructions (Figure 11 in Appendix E): (1) to ensure a story has a clear
ending or an essay has a clear stance and conclusion, and (2) to collaborate
with the system to write a story or an essay for at least ten minutes.

We used the following rubrics below to rate submissions in the qualification
round. The authors of this paper manually rated $201$ submissions with the
goal of qualifying writers who demonstrated that they could meet two
requirements. Writers whose submissions were rated as 4 or 5 were qualified to
participate in the main round. Note that writers who did not interact with the
system were automatically disqualified, since we wanted to confirm that the
writers were mindful of the requirement and demonstrated that they understood
the functionality and could interact with the system.

  * •

5: A great story with a clear ending, or a great essay with a clear stance and
conclusion

  * •

4: A reasonable story with a clear ending, or a reasonable essay with a clear
stance and conclusion

  * •

3: A story without a clear ending, or an essay without a clear stance and
conclusion

  * •

2: A below average story without an ending, or a below average essay without a
clear stance and conclusion

  * •

1: Spam

##  Appendix E Instructions

Figure 11 shows the instructions used for Amazon Mechanical Turk, specifically
the ones used for creative writing in the qualification round. The
instructions for the main round and argumentative writing were nearly
identical except for the first paragraph (which specified whether this is the
qualification round or main round) and specific wordings for stories and
essays.

Figure 11. Instructions used for Amazon Mechanical Turk.