Large Language Models (LLMs) are being rapidly adopted for many activities in organizations, business, and education. Included in their applications are capabilities to generate text, code, and models. This leads to questions about their potential role in the conceptual modeling part of information systems development. This paper reports on a panel presented at the 43rd International Conference on Conceptual Modeling where researchers discussed the current and potential role of LLMs in conceptual modeling. The panelists discussed applications and interest levels and expressed both optimism and caution in the adoption of LLMs. Suggested is a need for much continued research by the conceptual modeling community on LLM development and their role in research and teaching.

Presents an adaptive knowledge distillation framework to create efficient domain-specific language models.

Proceedings of the 43rd International Conference on Conceptual Modeling, presenting the latest advancements in the field.

Analyzes digital resilience in manufacturing industries during crises.

Explores dynamic personalization techniques to adapt conversational language models to user needs.

Proposes incorporating metabolic data into large language models to detect anomalies in clinical time-series data.

Crises send out early warning signals, mostly weak and difficult to detect amidst the noise of everyday life. Signal detection based on social media enables early identification of such signals supporting pro-active organizational responses before a crisis occurs. Nonetheless, social signal detection based on Twitter data is not applied in crisis management in practice as it is challenging due to the high volume of noise. With OSOS, we introduce a method for open-domain social signal detection of crisis-related indicators in tweets. OSOS works with multi-lingual Twitter data and combines multiple state-of-the-art models for data pre-processing (SoMaJo) and data filtration (GPT-3). It excels in crisis domains by leveraging fine-tuned GPT-3FT (Curie) model and achieving benchmark results in the CrisisBench dataset. The method was exemplified within a signaling service for crisis management. We were able to evaluate the proposed approach by means of a data set obtained from Twitter (X) in terms of performance in identifying potential social signals for energy-related crisis events.

Demonstrates how newspaper signals can be leveraged for predicting crises using natural language processing techniques.

Planning for complex tasks is a key task for knowledge workers that is often time-consuming and depends on the manual extraction of knowledge from documents. In this research, we propose an end-to-end method, called PlanKG, that: (1) extracts knowledge graphs from full-text plan descriptions (FTPD); and (2) generates novel FTPD according to plan requirements and context information provided by users. From the knowledge graphs, activity sequences are obtained and projected into embedding spaces. We show that compressed activity sequences are sufficient for the search and generation of plan descriptions. The PlanKG method uses a pipeline consisting of decoder-only transformer models and encoder-only transformer models. To evaluate the PlanKG method, we conducted an experimental study for movie plot descriptions and compared our method with original FTPDs and FTPD summarizations. The results of this research has significant potential for enhancing efficiency and precision when searching and generating plans.

This paper explores the integration of quantum computing into service ecosystems to enhance manufacturing simulations.

Introduces quantum feature embeddings for improving the performance of graph neural networks.

REAVER introduces an attention-based sliding-window spectogram approach for real-time earthquake prediction.

This paper presents a Self Attention-based Convolutional Neural Network (SACNN) designed to detect fraudulent behavior in sports data.

Uses semantic priming and knowledge graphs to address and mitigate errors in language models.

Discusses the challenges and opportunities in requirements engineering for quantum computing in manufacturing.