Large language models (LLMs) are capable of successfully performing many language
processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify …

We perform an interdisciplinary large-scale evaluation for detecting lexical semantic
divergences in a diachronic and in a synchronic task: semantic sense changes across time …

Language use differs between domains and even within a domain, language use changes
over time. For pre-trained language models like BERT, domain adaptation through …

While both agent interaction and personalisation are vibrant topics in research on large
language models (LLMs), there has been limited focus on the effect of language interaction …

The field of NLP has made substantial progress in building meaning representations.
However, an important aspect of linguistic meaning, social meaning, has been largely …

Much previous work characterizing language variation across Internet social groups has
focused on the types of words used by these groups. We extend this type of study by …

This study investigates the effectiveness of large language models (LLMs), specifically
ChatGPT, in machine translation (MT) and compares them with traditional tools like Google …

Human and Computational Measurement of Lexical Semantic Change Page 1 1 Human
and Computational Measurement of Lexical Semantic Change March 24, 2022 Dominik …

Abstract We introduce the Reddit Politosphere, a large-scale resource of online political
discourse covering more than 600 political discussion groups over a period of 12 years. It is …

Toxic content is a global problem, but most resources for detecting toxic content are in
English. When datasets are created in other languages, they often focus exclusively on one …