Conference proceeding
The HaLLMark Effect: Supporting Provenance and Transparent Use of Large Language Models in Writing with Interactive Visualization
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, pp.1-15
ACM Conferences
CHI '24: CHI Conference on Human Factors in Computing Systems
05/11/2024
DOI: 10.1145/3613904.3641895
Abstract
The use of Large Language Models (LLMs) for writing has sparked controversy both among readers and writers. On one hand, writers are concerned that LLMs will deprive them of agency and ownership, and readers are concerned about spending their time on text generated by soulless machines. On the other hand, AI-assistance can improve writing as long as writers can conform to publisher policies, and as long as readers can be assured that a text has been verified by a human. We argue that a system that captures the provenance of interaction with an LLM can help writers retain their agency, conform to policies, and communicate their use of AI to publishers and readers transparently. Thus we propose HaLLMark, a tool for visualizing the writer’s interaction with the LLM. We evaluated HaLLMark with 13 creative writers, and found that it helped them retain a sense of control and ownership of the text.
Details
- Title: Subtitle
- The HaLLMark Effect: Supporting Provenance and Transparent Use of Large Language Models in Writing with Interactive Visualization
- Creators
- Md Naimul Hoque - University of Maryland, College ParkTasfia Mashiat - George Mason UniversityBhavya Ghai - Amazon (United States)Cecilia D. Shelton - University of Maryland, College ParkFanny Chevalier - University of TorontoKari Kraus - University of Maryland, College ParkNiklas Elmqvist - Aarhus University
- Contributors
- Florian Floyd Mueller (Editor) - Monash UniversityPenny Kyburz (Editor) - Australian National UniversityJulie R. Williamson (Editor) - University of GlasgowCorina Sas (Editor) - Lancaster UniversityMax L. Wilson (Editor) - University of NottinghamPhoebe Toups Dugas (Editor) - Monash UniversityIrina Shklovski (Editor) - University of Copenhagen
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, pp.1-15
- Conference
- CHI '24: CHI Conference on Human Factors in Computing Systems
- Series
- ACM Conferences
- DOI
- 10.1145/3613904.3641895
- Publisher
- ACM; NEW YORK
- Number of pages
- 15
- Grant note
- U.S. National Science Foundation: IIS-2211628
While this work deals with AI co-writing, none of it was written using an AI model such as GPT-4. In other words, HALLMARK was actually not used in producing the copy in this paper. This work was partly supported by grant IIS-2211628 from the U.S. National Science Foundation. Any opinions, fndings, and conclusions or recommendations expressed here are those of the authors and do not necessarily refect the views of the funding agency.
- Language
- English
- Date published
- 05/11/2024
- Academic Unit
- Computer Science
- Record Identifier
- 9984787459302771
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