We present a collection of human association norms to German personal name compounds (PNCs) such as “Tore-Klose” (goal-Klose) and corresponding full names (Miroslav Klose), thus providing a novel testbed for PNC evaluation, i.e., analogical vs. contrastive positive vs. negative perception effects. The associations are obtained in an online experiment with German native speakers, analyzed regarding our novel intertwined PNC–person association setup, and accompanied by an LLM synthetic generation approach for augmentation.

As LLMs become widespread, trust in their behavior becomes increasingly important. For NLP research, it is crucial to ensure that not only AI designers and developers, but also end users, are enabled to control the properties of trustworthy LLMs, such as transparency, privacy, or accuracy. However, involving end users in this process remains a practical challenge. Based on a design-centered survey of methods developed in recent papers from HCI and NLP venues, this paper proposes seven design paradigms that can be integrated in NLP research to enhance end-user control over the trustworthiness of LLMs. We discuss design gaps and challenges of applying these paradigms in NLP and propose future research directions.

We present a comprehensive computational study of the under-investigated phenomenon of personal name compounds (PNCs) in German such as Willkommens-Merkel (‘Welcome-Merkel’). Prevalent in news, social media, and political discourse, PNCs are hypothesized to exhibit an evaluative function that is reflected in a more positive or negative perception as compared to the respective personal full name (such as Angela Merkel). We model 321 PNCs and their corresponding full names at discourse level, and show that PNCs bear an evaluative nature that can be captured through a variety of computational methods. Specifically, we assess through valence information whether a PNC is more positively or negatively evaluative than the person’s name, by applying and comparing two approaches using (i) valence norms and (ii) pre-trained language models (PLMs). We further enrich our data with personal, domain-specific, and extra-linguistic information and perform a range of regression analyses revealing that factors including compound and modifier valence, domain, and political party membership influence how a PNC is evaluated.