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.

Aspect-category sentiment analysis provides granular insights by identifying specific themes within product reviews that are associated with particular opinions. Supervised learning approaches dominate the field. However, data is scarce and expensive to annotate for new domains. We argue that leveraging large language models in a zero-shot setting is beneficial where the time and resources required for dataset annotation are limited. Furthermore, annotation bias may lead to strong results using supervised methods but transfer poorly to new domains in contexts that lack annotations and demand reproducibility. In our work, we propose novel techniques that combine multiple chain-of-thought agents by leveraging large language models’ token-level uncertainty scores. We experiment with the 3B and 70B+ parameter size variants of Llama and Qwen models, demonstrating how these approaches can fulfil practical needs and opening a discussion on how to gauge accuracy in label-scarce conditions.