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Vision Language Models (VLMs) and Unsolvable Problem Detection (UPD)
In today’s world, Vision Language Models (VLMs) are revolutionizing machine learning by integrating visual and textual understanding. However, concerns about their reliability have emerged. To address this, researchers have proposed UPD, a task to evaluate a VLM’s ability to recognize and refrain from answering unsolvable questions.
Challenges and Solutions
The challenge of UPD lies in the need for VLMs to recognize incompatible questions and withhold answers. Researchers have identified three distinct problem types within UPD:
- Absent Answer Detection (AAD): Testing the model’s ability to recognize when the correct answer is absent from the provided choices.
- Incompatible Answer Set Detection (IASD): Evaluating the model’s capacity to identify when the answer set is entirely irrelevant to the context.
- Incompatible Visual Question Detection (IVQD): Assessing the model’s understanding of the alignment between visual content and textual questions.
To explore these problem types, researchers adapted the MMBench dataset, creating benchmarks tailored for AAD, IASD, and IVQD. The findings reveal that most VLMs struggle with UPD, even larger models like GPT-4V and LLaVA-Next-34B exhibit limitations in certain abilities and settings.
Researchers have explored prompt engineering strategies and instruction tuning to improve VLM performance for UPD. However, the effectiveness of these strategies varied among different VLMs, highlighting the complexity of the challenge.
Future Work and Practical Applications
Future work may explore chain-of-thought reasoning, extension to expert-level questions, and the development of post-hoc detection methods to enhance the trustworthiness of VLMs.
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