@misc{wu_promise_2023,
	title = {\{PROMISE}: {A} {Framework} for {Model}-{Driven} {Stateful} {Prompt} {Orchestration}},
	shorttitle = ,
	url = {http://arxiv.org/abs/2312.03699},
	doi = {10.48550/arXiv.2312.03699},
	abstract = {The advent of increasingly powerful language models has raised expectations for language-based interactions. However, controlling these models is a challenge, emphasizing the need to be able to investigate the feasibility and value of their application. We present PROMISE, a framework that facilitates the development of complex language-based interactions with information systems. Its use of state machine modeling concepts enables model-driven, dynamic prompt orchestration across hierarchically nested states and transitions. This improves the control of the behavior of language models and thus enables their effective and efficient use. We show the benefits of PROMISE in the context of application scenarios within health information systems and demonstrate its ability to handle complex interactions.},
	urldate = {2023-12-10},
	publisher = {arXiv},
	author = {Wu, Wenyuan and Heierli, Jasmin and Meisterhans, Max and Moser, Adrian and Färber, Andri and Dolata, Mateusz and Gavagnin, Elena and de Spindler, Alexandre and Schwabe, Gerhard},
	month = dec,
	year = {2023},
	note = {arXiv:2312.03699 [cs]},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: Minor revision regards wording},
}