title: Unsolvable Problem Detection: Evaluating Trustworthiness of Vision
    Language Models
summary: 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.
paper_authors._id: 660a1b750b7d615a56a13e19
paper_authors.name: Atsuyuki Miyai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-11T05:51:49.719Z
paper_authors._id: 6527b37c0ae663e384eb1b85
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paper_authors.fullname: Atsuyuki Miyai
paper_authors.type: user
paper_authors.user: AtsuMiyai
paper_authors._id: 660a1b750b7d615a56a13e1a
paper_authors.name: Jingkang Yang
paper_authors._id: 660a1b750b7d615a56a13e1b
paper_authors.name: Jingyang Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-05T10:04:29.626Z
paper_authors._id: 63f7773ed36951307fd02d18
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63f7773ed36951307fd02d18/l8KmKNYvBXbUDHaH9BWL6.jpeg
paper_authors.fullname: Jingyang Zhang
paper_authors.type: user
paper_authors.user: zjysteven
paper_authors._id: 660a1b750b7d615a56a13e1c
paper_authors.name: Yifei Ming
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-11T07:46:20.294Z
paper_authors._id: 641787ad1f1f3b0fa80f5340
paper_authors.avatarUrl: /avatars/489b7c999bce2160e27c9b487e1026ef.svg
paper_authors.fullname: yifei ming
paper_authors.type: user
paper_authors.user: alvinming
paper_authors._id: 660a1b750b7d615a56a13e1d
paper_authors.name: Qing Yu
paper_authors._id: 660a1b750b7d615a56a13e1e
paper_authors.name: Go Irie
paper_authors._id: 660a1b750b7d615a56a13e1f
paper_authors.name: Yixuan Li
paper_authors._id: 660a1b750b7d615a56a13e20
paper_authors.name: Hai Li
paper_authors._id: 660a1b750b7d615a56a13e21
paper_authors.name: Ziwei Liu
paper_authors._id: 660a1b750b7d615a56a13e22
paper_authors.name: Kiyoharu Aizawa
paper_title: Unsolvable Problem Detection: Evaluating Trustworthiness of Vision
    Language Models
paper_summary: 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.
paper_ai_keywords: Vision Language Models (VLMs) Unsolvable Problem Detection (UPD) Visual Question Answering (VQA) Absent Answer Detection (AAD) Incompatible Answer Set Detection (IASD) Incompatible Visual Question Detection (IVQD) GPT-4V LLaVA-Next-34B