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, [log in to unmask]
- Jennifer J. Xu, Bentley University, [log in to unmask]
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, [log in to unmask]
· Jennifer J. Xu, [log in to unmask]
*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.
--
Michael Chau
Professor in Innovation and Information Management
HKU Business School
The University of Hong Kong
---------------
To unsubscribe from the DBWorld mailing list please visit https://dbworld.sigmod.org and follow the unsubscribe directions given there.
|
