Elicitation Method

Elicitation methods aim to efficiently and effectively extract information, preferences, or knowledge from various sources, including humans, machines, and data. Current research focuses on developing adaptive and personalized elicitation strategies, often employing machine learning models like Bayesian networks and large language models (LLMs) to optimize the questioning process and improve information extraction accuracy. These advancements are impacting diverse fields, from recommender systems and decision support to human-computer interaction and the development of more robust and explainable AI systems. The ultimate goal is to minimize the effort required to obtain high-quality information while maximizing its relevance and utility.