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Dear Colleagues,
ACM Transactions on Management Information Systems (TMIS) is
delighted to
announce a new special issue
on Impacts of Large Language Models on Business and Management:
https://dl.acm.org/pb-assets/static_journal_pages/tmis/calls_for_papers/ACM-
TMIS-CFP-LLMs-2023.pdf .
*Submission Deadline*: December 31, 2023
*Guest Editors:*
- Michael Chau, The University of Hong Kong,
mchau@business.hku.hk
- Jennifer J. Xu, Bentley University,
jxu@bentley.edu
Large language models (LLMs)—deep neural networks pre-trained
using a vast
amount of unlabeled text data—have
advanced substantially in the past few years. These LLMs, such as
BERT
(Bidirectional Encoder Representations from
Transformers) (Devlin et al. 2018) and GPT (Generative Pretrained
Transformers) (Radford et al. 2018), often contain
millions or billions of parameters and have achieved outstanding
performance
in a wide variety of natural language
processing (NLP) tasks, including document classification, speech
recognition, machine translation, and named entity
recognition. In particular, recent launches of general
conversation-based
LLMs, such as OpenAI’s ChatGPT, Google’s Bard,
BigScience’s BLOOM, and Baidu’s ErnieBot, have taken the world by
storm,
gaining massive attention from not only
academics and practitioners but also the general public due to
their
remarkable capabilities of understanding natural
languages and producing high-quality responses for tasks that go
beyond
traditional NLP tasks.
Many believe that LLMs are one of the greatest milestones of
artificial
intelligence (AI) and have the potential to
become a big game changer to unleash tremendous technological,
economic, and
societal revolutions. Many enterprises
and organizations are already preparing for the radical changes
that may be
brought by applications and adoptions of
LLMs, such as automation of routine or mundane tasks and
significant
reduction in workforce. For example, LLMs may be
integrated into customer relationship management applications to
automatically handle queries, requests, and complaints
while providing a seamless conversational user experience. By
adopting and
applying LLMs in a timely, strategic manner,
enterprises and organizations can enhance decision making, improve
productivity, and reduce costs. Individuals can also
benefit from applications of LLMs. For instance, given proper
prompts and
instructions, ChatGPT can offer advice on the
stock market, help people write emails, plan vacations, solve
problems, and
even code or debug software programs (Thorp
2023). As LLMs are being adopted rapidly worldwide, they will also
bring
broader impacts on society.
A plethora of research opportunities are emerging for scholars in
various
disciplines including information systems (IS).
IS researchers can study and make contributions to the literature
on many
interesting research questions, such as the
design of systems based on LLMs to solve business problems, the
behavioral
and technical aspects of human-AI interaction,
and the ethical and safety issues in using LLMs. As many thought
leaders and
scholars have pointed out, LLMs could be a
double-edged sword, bringing both opportunities and challenges to
many areas
and domains, ranging from business, finance,
healthcare and medicine, education to law and policy (Kasneci et
al. 2023;
Shen et al. 2023). Therefore, investigations of
possible negative effects of LLMs, such as the hallucination
problem in
which an LLM provides false or inaccurate
information (Azamfirei et al. 2023), can also shed lights on the
limitations
of current LLMs and the design of future AI,
which should be helpful, honest, and harmless (Bai et al. 2022).
*Topics*
The aim of this special issue is to curate a set of high-quality
papers that
focus on the design and application of LLMs in
business and management as well as ethical and social issues
involved. The
special issue is open to researchers using diverse
research methods, including quantitative, qualitative,
algorithmic,
analytical modeling, predictive modeling, and design
science. It is also open to research conducted at an individual,
group,
organizational, and societal level. Topics of
interest include but are not limited to the following:
· Design and evaluation of LLM applications in business and
management
· The use of LLMs in system analysis, design, and development
· The impact of LLMs on consumer perception and behavior
· LLM-enabled decision making
· Measuring the business value of LLMs
· Using LLMs for sentiment analysis in business and finance
· Applications of LLMs in process automation
· Safe use of LLMs
· The dark side of LLMs and the ethical issues related to the use
ofLLMs
· Interactions between humans and LLMs
· Human-in-the-loop in the design and application of LLMs
· Evaluation of emerging LLM designs such as sparse expert models
and
in-context learning
*Important Dates*
· Open for Submissions: September 1, 2023
· Submissions deadline: December 31, 2023
· First-round review decisions: February 28, 2024
· Deadline for revision submissions: May 15, 2024
· Notification of final decisions: September 30, 2024
· Tentative publication: March 2025
*Submission Information*
All submissions will follow ACM TMIS guidelines (
https://dl.acm.org/journal/tmis/author-guidelines)
and submitted through the TMIS portal
(
https://mc.manuscriptcentral.com/tmis),
selecting the paper type for submission called “Special Issue on
Impacts of
Large Language Models on Business and Management.”
For questions and further information, please contact guest
editors at:
· Michael Chau,
mchau@business.hku.hk
· Jennifer J. Xu,
jxu@bentley.edu
*References*
Azamfirei, R., Kudchadkar, S.R., and Fackler, J. 2023. "Large
language
models and the perils of their hallucinations," Critical
Care (27) 120.
Bai, Y., Jones, A., Ndousse, K., et al. 2022."Training a helpful
and
harmless assistant with reinforcement learning from human
feedback," arXiv:2204.05862.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. 2018."BERT:
Pre-training of deep bidirectional transformers for language
understanding," arXiv:1810.04805.
Kasneci, E., Sessler, K., Küchemann, S., et al. 2023. "ChatGPT for
good? On
opportunities and challenges of large language
models for education,"
Learning and Individual Differences (103) 102274.
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I.
2018."Improving
language understanding by generative pre-training,"
from
https://cdn.openai.com/research-covers/language-unsupervised/language_unders
tanding_paper.pdf.
Shen, Y., Heacock, L., Elias, J., et al. 2023. "ChatGPT and other
large
language models are double-edged swords," Radiology
(307:2) e230163.
Thorp, H.H. 2023. "ChatGPT is fun, but not an author," Science
(379), pp.
313-313.
--
Michael Chau
Professor in Innovation and Information Management
HKU Business School
The University of Hong Kong
http://www.business.hku.hk/~mchau/
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