4 research outputs found
Supporting Human Cognitive Writing Processes: Towards a Taxonomy of Writing Support Systems
In the field of natural language processing (NLP), advances in transformer architectures and large-scale language models have led to a plethora of designs and research on a new class of information systems (IS) called writing support systems, which help users plan, write, and revise their texts. Despite the growing interest in writing support systems in research, there needs to be more common knowledge about the different design elements of writing support systems. Our goal is, therefore, to develop a taxonomy to classify writing support systems into three main categories (technology, task/structure, and user). We evaluated and refined our taxonomy with seven interviewees with domain expertise, identified three clusters in the reviewed literature, and derived five archetypes of writing support system applications based on our categorization. Finally, we formulate a new research agenda to guide researchers in the development and evaluation of writing support systems
Creativity and Machine Learning: a Survey
There is a growing interest in the area of machine learning and creativity.
This survey presents an overview of the history and the state of the art of
computational creativity theories, machine learning techniques, including
generative deep learning, and corresponding automatic evaluation methods. After
presenting a critical discussion of the key contributions in this area, we
outline the current research challenges and emerging opportunities in this
field.Comment: 25 pages, 3 figures, 2 table
How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries
Generative AI is expected to have transformative effects in multiple
knowledge industries. To better understand how knowledge workers expect
generative AI may affect their industries in the future, we conducted
participatory research workshops for seven different industries, with a total
of 54 participants across three US cities. We describe participants'
expectations of generative AI's impact, including a dominant narrative that cut
across the groups' discourse: participants largely envision generative AI as a
tool to perform menial work, under human review. Participants do not generally
anticipate the disruptive changes to knowledge industries currently projected
in common media and academic narratives. Participants do however envision
generative AI may amplify four social forces currently shaping their
industries: deskilling, dehumanization, disconnection, and disinformation. We
describe these forces, and then we provide additional detail regarding
attitudes in specific knowledge industries. We conclude with a discussion of
implications and research challenges for the HCI community.Comment: 40 pages, 5 tables, 6 figure
Art or Artifice? Large Language Models and the False Promise of Creativity
Researchers have argued that large language models (LLMs) exhibit
high-quality writing capabilities from blogs to stories. However, evaluating
objectively the creativity of a piece of writing is challenging. Inspired by
the Torrance Test of Creative Thinking (TTCT), which measures creativity as a
process, we use the Consensual Assessment Technique [3] and propose the
Torrance Test of Creative Writing (TTCW) to evaluate creativity as a product.
TTCW consists of 14 binary tests organized into the original dimensions of
Fluency, Flexibility, Originality, and Elaboration. We recruit 10 creative
writers and implement a human assessment of 48 stories written either by
professional authors or LLMs using TTCW. Our analysis shows that LLM-generated
stories pass 3-10X less TTCW tests than stories written by professionals. In
addition, we explore the use of LLMs as assessors to automate the TTCW
evaluation, revealing that none of the LLMs positively correlate with the
expert assessments