The proliferation of large language models (LLMs) has significantly increased the potential for automated dissemination of disinformation, necessitating robust systems for check-worthiness detection. However, existing models are primarily trained on human claims, leaving their performance on machine-generated text largely unexplored. In this paper, we benchmark encoder models (BERT and RoBERTa) and industry accessible tools (ClaimBuster) against LLM-paraphrased claims across three stylistic categories: syntactic restructuring, syntactic complexity and lexical informality. Our results indicate a consistent performance degradation on synthetic claims, particularly on complex and informal claims. We demonstrate that adversarial training significantly improves model resilience, with RoBERTa achieving F1-score gains up to +5.22 on the CheckIt dataset. Finally, SHAP analysis reveals that while base models rely on narrow syntactic heuristics such as active voice, robust models learn to anchor their prediction on core factual entities. These findings highlight the necessity of stylistic-aware training to maintain fact-checking efficacy in an increasingly LLM-populated information landscape.

Anthropomorphism is a literary device where human-like characteristics are used to refer to non-human entities. However, the use of anthropomorphism in the scientific description and public communication of large language models could lead to misunderstanding amongst scientists and lay-people regarding the technical capabilities and limitations of these models. In this study, we present an analysis of anthropomorphised language commonly used to describe LLMs, showing that the presence of terms such as ‘learn’, ‘achieve’, ‘predict’ and ‘can’ are typically correlated with human labels of anthropomorphism. We also perform experiments to develop a classification system for anthropomorphic descriptions of LLMs in scientific writing at the sentence level. We find that whilst a supervised Roberta-based system identifies anthropomorphisms with F1-score of 0.564, state-of-the-art LLM-based approaches regularly overfit to the task.

We investigate the prevalence of anthropomorphic language in the reporting of AI technology, focussed on NLP and LLMs. We undertake a corpus annotation focussing on one year of ACL long-paper abstracts and news articles from the same period. We find that 74% of ACL abstracts and 88% of news articles contain some form of anthropomorphic description of AI technology. Further, we train a regression classifier based on BERT, demonstrating that we can automatically label abstracts for their degree of anthropomorphism based on our corpus. We conclude by applying this labelling process to abstracts available in the entire history of the ACL Anthology and reporting on diachronic and inter-venue findings, showing that the degree of anthropomorphism is increasing at all examined venues over time.

Many enterprises are increasingly adopting Artificial Intelligence (AI) to make internal processes more competitive and efficient. In response to public concern and new regulations for the ethical and responsible use of AI, implementing AI governance frameworks could help to integrate AI within organisations and mitigate associated risks. However, the rapid technological advances and lack of shared ethical AI infrastructures creates barriers to their practical adoption in businesses. This paper presents a real-world AI application at TVS Supply Chain Solutions, reporting on the experience developing an AI assistant underpinned by large language models and the ethical, regulatory, and sociotechnical challenges in deployment for enterprise use.