[AISWorld] CFP: Special Issue on Impacts of Large Language Models at ACM Transactions on Management Information Systems (TMIS)

[Jennifer Xu]

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 at business.hku.hk
   - Jennifer J. Xu, Bentley University, jxu at 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 of
LLMs
·       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 at business.hku.hk
·       Jennifer J. Xu, jxu at bentley.edu


*Special Issue Editorial Board*
·       Victor Benjamin, Arizona State University
·       Yidong Chai, Hefei University of Technology
·       Ioanna Constantiou, Copenhagen Business School
·       Raymond Lau, City University of Hong Kong
·       Jingjing Li, University of Virginia
·       Wenwen Li, Fudan University
·       Xiao Liu, Arizona State University
·       Sagar Samtani, Indiana University
·       Timm Teubner, Einstein Center Digital Future
·       G. Alan Wang, Virginia Tech
·       Yi Yang, Hong Kong University of Science and Technology
·       Kunpeng Zhang, The University of Maryland
·       Yilu Zhou, Fordham University



*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_understanding_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.


Jennifer Jie Xu, Ph.D.
Professor of Computer Information Systems
Bentley University
Waltham, MA 02452
Tel: 781-891-2711