[go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Human-Computer Interaction

arXiv:2309.10433 (cs)
[Submitted on 19 Sep 2023 (v1), last revised 20 Feb 2024 (this version, v2)]

Title:Writer-Defined AI Personas for On-Demand Feedback Generation

Authors:Karim Benharrak, Tim Zindulka, Florian Lehmann, Hendrik Heuer, Daniel Buschek
View PDF HTML (experimental)
Abstract:Compelling writing is tailored to its audience. This is challenging, as writers may struggle to empathize with readers, get feedback in time, or gain access to the target group. We propose a concept that generates on-demand feedback, based on writer-defined AI personas of any target audience. We explore this concept with a prototype (using GPT-3.5) in two user studies (N=5 and N=11): Writers appreciated the concept and strategically used personas for getting different perspectives. The feedback was seen as helpful and inspired revisions of text and personas, although it was often verbose and unspecific. We discuss the impact of on-demand feedback, the limited representativity of contemporary AI systems, and further ideas for defining AI personas. This work contributes to the vision of supporting writers with AI by expanding the socio-technical perspective in AI tool design: To empower creators, we also need to keep in mind their relationship to an audience.
Comments: 18 pages, 8 figures, 3 tables
Subjects: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
ACM classes: H.5.2; I.2.7
Cite as: arXiv:2309.10433 [cs.HC]
  (or arXiv:2309.10433v2 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2309.10433
Related DOI: https://doi.org/10.1145/3613904.3642406

Submission history

From: Daniel Buschek [view email]
[v1] Tue, 19 Sep 2023 08:49:35 UTC (2,403 KB)
[v2] Tue, 20 Feb 2024 15:20:32 UTC (5,107 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.HC
< prev   |   next >
new | recent | 2023-09
Change to browse by:
cs
cs.CL

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)