Connor Lawless

Super excited about this project from my time at @MSFTResearch! We explore how to combine LLMs and Constraint Programming to allow non-expert users to design optimization models in the context of meeting scheduling.

"I Want It That Way": Enabling Interactive Decision Support Using Large Language Models and Constraint Programming paper page: huggingface.co/papers/2312.06… A critical factor in the success of decision support systems is the accurate modeling of user preferences. Psychology research has demonstrated that users often develop their preferences during the elicitation process, highlighting the pivotal role of system-user interaction in developing personalized systems. This paper introduces a novel approach, combining Large Language Models (LLMs) with Constraint Programming to facilitate interactive decision support. We study this hybrid framework through the lens of meeting scheduling, a time-consuming daily activity faced by a multitude of information workers. We conduct three studies to evaluate the novel framework, including a diary study (n=64) to characterize contextual scheduling preferences, a quantitative evaluation of the system's performance, and a user study (n=10) with a prototype system. Our work highlights the potential for a hybrid LLM and optimization approach for iterative preference elicitation and design considerations for building systems that support human-system collaborative decision-making processes.

🧵RT 🙏 SEASON5 of the #MachineLearning NeEDS #MathematicalOptimization Online Seminar Series, congreso.us.es/mlneedsmo/, has ended! We thank the 8⃣8⃣ speakers from 2⃣1⃣ countries that we have had since 2021 ! @REA_research @EUROonline_News @imus_us @CBS_ECON @needs_project #orms