  Large vision-language models (LVLMs), while proficient in following
instructions and responding to diverse questions, invariably generate detailed
responses even when questions are ambiguous or unanswerable, leading to
hallucinations and bias issues. Thus, it is essential for LVLMs to proactively
engage with humans to ask for clarifications or additional information for
better responses. In this study, we aim to shift LVLMs from passive answer
providers to proactive engaged partners. We begin by establishing a
three-tiered hierarchy for questions of invalid, ambiguous, and personalizable
nature to measure the proactive engagement capabilities of LVLMs. Utilizing
this hierarchy, we create PIE, (ProactIve Engagement Evaluation) through GPT-4o
and human annotators, consisting of 853 questions across six distinct,
fine-grained question types that are verified by human annotators and
accompanied with well-defined metrics. Our evaluations on \benchmark indicate
poor performance of existing LVLMs, with the best-performing open-weights model
only achieving an Aggregate Align Rate (AAR) of 0.28. In response, we introduce
MACAROON, self-iMaginAtion for ContrAstive pReference OptimizatiON, which
instructs LVLMs to autonomously generate contrastive response pairs for
unlabeled questions given the task description and human-crafted criteria.
Then, the self-imagined data is formatted for conditional reinforcement
learning. Experimental results show MACAROON effectively improves LVLMs'
capabilities to be proactively engaged (0.84 AAR) while maintaining comparable
performance on general tasks.
