  The future of conversational agents will provide users with personalized
information responses. However, a significant challenge in developing models is
the lack of large-scale dialogue datasets that span multiple sessions and
reflect real-world user preferences. Previous approaches rely on experts in a
wizard-of-oz setup that is difficult to scale, particularly for personalized
tasks. Our method, LAPS, addresses this by using large language models (LLMs)
to guide a single human worker in generating personalized dialogues. This
method has proven to speed up the creation process and improve quality. LAPS
can collect large-scale, human-written, multi-session, and multi-domain
conversations, including extracting user preferences. When compared to existing
datasets, LAPS-produced conversations are as natural and diverse as
expert-created ones, which stays in contrast with fully synthetic methods. The
collected dataset is suited to train preference extraction and personalized
response generation. Our results show that responses generated explicitly using
extracted preferences better match user's actual preferences, highlighting the
value of using extracted preferences over simple dialogue history. Overall,
LAPS introduces a new method to leverage LLMs to create realistic personalized
conversational data more efficiently and effectively than previous methods.
