Exploring Supervised Approaches to the Detection of Anthropomorphic Language in the Reporting of NLP Venues

We investigate the prevalence of anthropomorphic language in the reporting of AI technology, focussed on NLP and LLMs. Anthropomorphised description of LLM/AI technology has the potential to misrepresent the capabilities of our field to other scientists, policy makers and the public. 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.