LLM-Resilient Bibliometrics: Factual Consistency Through Entity Triplet Extraction

Abstract: The increase in power and availability of Large Language Models (LLMs) since late 2022 led to increased concerns with their usage to automate academic paper mills. In turn, this poses a threat to bibliometrics-based technology monitoring and forecasting in...

Identifying Emerging Technologies and Influential Companies Using Network Dynamics of Patent Clusters

Abstract The need for dependable and real-time insights on technological paradigm shifts requires objective information. We develop a lean recommender system which predicts emerging technology by a sequential blend of machine learning and network analytics. We...

Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense

Abstract Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users’ expectations of interactions with AI (conversational models). Arguably the focal point of...

Identifying Emerging Technologies and Leading Companies using Network Dynamics of Patent Clusters: a Cybersecurity Case Study

Abstract Strategic decisions rely heavily on non-scientific instrumentation to forecast emerging technologies and leading companies. Instead, we build a fast quantitative system with a small computational footprint to discover the most important technologies and...