Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models
Summary
The article surveys basic limitations of transformer-based language models, highlighting hallucinations, inconsistency, and prompt sensitivity as enduring challenges. It discusses evaluation gaps and safety concerns, advocating retrieval-augmented generation and tool integration to improve reliability in high-stakes settings. For content creators, it provides actionable guidance on building more trustworthy AI-enabled business automation and IT workflows.