NLP Large Language Models have had a deep impact not only on research but also on society. However, there is one problem underlying their own nature which forbids them to be fully trusted and accountable: Hallucinations – a phenomenon that calls for a scientific account in the form of a theoretical model that represents the underlying rationale of how it emerges. In this paper, we present such a model. In the same spirit as in other fields proposing models of natural phenomena – such as rainfall – or social phenomena – such as knowledge creation in organizations – we propose a theoretical model that serves to explain hallucinations by synthesizing in an abstract and structured framework how the interrelations and interactions of more than 50 factors give rise to this phenomenon helping us to better understand its complexity and idiosyncrasies.

This paper presents our winning system for SemEval-2026 Task 5 on rating the plausibility of word senses in ambiguous stories. Unlike traditional Word Sense Disambiguation, the task requires predicting continuous plausibility scores that reflect human variability rather than selecting a single correct sense. We propose a multi-target fine-tuning framework for decoder-only large language models that jointly optimizes regression for score prediction and text generation for interpretable explanations. To address inter-annotator variability, we adopt objective-level strategies to enhance robustness. Our system achieves first place, demonstrating the effectiveness of unified regressive–generative modeling for fine-grained plausibility estimation.