The field of large language models is moving towards a deeper understanding of the underlying structures and mechanisms that govern their behavior. Researchers are exploring the emergence of lexical semantics, the impact of lexical training data coverage on hallucination detection, and the development of data-centric approaches to multilingual hallucination detection. These efforts aim to improve the reliability and accuracy of large language models, particularly in open-domain question answering and scientific text generation. Notable papers in this area include:

• A study that derived a unified model linking word lengths, vocabulary growth, and rank-frequency structure, providing a structurally grounded null model for natural-language word statistics.
• A data-centric approach to multilingual hallucination detection that achieved competitive performance across multiple languages by addressing training data scarcity and imbalance.
• An investigation of Martin's Law in text generated by neural language models, which revealed a non-monotonic developmental trajectory and established a novel methodology for evaluating emergent linguistic structure.