At the current state of the art, however, there is the major problem of “hallucinations” of LLMs, i.e., replying with pieces of information that are not based on factual knowledge but rather stem from more “creative” generations by the language model.

TrustInLLM includes research on a new approach to Requirements Engineering for specifying the needs in this regard by integrating quality attributes (in the sense of properties) of both systems (in our case, LLM-assisted systems for querying technical documentation) and data (in our case, primarily technical documentation) with each other, since properties of both system (such as transparency) and data (such as correctness) are relevant.

The IFZ team is primarily concerned with the diversity-sensitive, human-centered development and evaluation of trustworthy AI by involving stakeholders and users.