The field of natural language processing is moving towards a deeper understanding of how language models handle truth and misinformation. Recent studies have investigated the ability of humans to detect AI-generated fake news, with findings suggesting that cultural familiarity can aid in the verification of authentic information but may also introduce bias when evaluating fabricated content. Additionally, research has focused on the evaluation of large language models (LLMs) in various domains, including their ability to advise students on study-abroad questions and their capacity to produce creative texts such as poetry. Notably, LLMs have been found to be capable of producing poetry that is indistinguishable from human-written poetry, even in low-resource languages. Furthermore, the stability of LLMs' internal representations of truth has been assessed, revealing that these models can be fragile when faced with unfamiliar statements. Overall, the field is advancing towards a more nuanced understanding of the strengths and limitations of LLMs and their potential applications. Noteworthy papers include: Representational Stability of Truth in Large Language Models, which introduced a diagnostic for auditing and training LLMs to preserve coherent truth assignments under semantic uncertainty. The paper Domain-Grounded Evaluation of LLMs in International Student Knowledge provided a clear overview of how current LLMs behave in a specific domain, evaluating their accuracy and hallucination rates in advising students on study-abroad questions.