Unsolvable Problem Detection: Evaluating Trustworthiness of Vision
  Language Models
Atsuyuki Miyai Jingkang Yang Jingyang Zhang Yifei Ming Qing Yu Go Irie Yixuan Li Hai Li Ziwei Liu Kiyoharu Aizawa
This paper introduces a novel and significant challenge for Vision Language
Models (VLMs), termed Unsolvable Problem Detection (UPD). UPD examines the
VLM's ability to withhold answers when faced with unsolvable problems in the
context of Visual Question Answering (VQA) tasks. UPD encompasses three
distinct settings: Absent Answer Detection (AAD), Incompatible Answer Set
Detection (IASD), and Incompatible Visual Question Detection (IVQD). To deeply
investigate the UPD problem, extensive experiments indicate that most VLMs,
including GPT-4V and LLaVA-Next-34B, struggle with our benchmarks to varying
extents, highlighting significant room for the improvements. To address UPD, we
explore both training-free and training-based solutions, offering new insights
into their effectiveness and limitations. We hope our insights, together with
future efforts within the proposed UPD settings, will enhance the broader
understanding and development of more practical and reliable VLMs.