Idan Szpektor
2 indexed papers
Recent (6 mo)
2With code
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226
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NLP×2AI×2Vision×1
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2026
Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?
This paper introduces a new evaluation framework, SpatialUncertain, demonstrating that current Vision-Language Models (VLMs) are prone to overconfident and incorrect answers to spatial questions when visual evidence is incomplete or misleading.
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
This paper introduces two novel mechanisms, reinforcement learning with metacognitive feedback (RLMF) and metacognitive data selection, to enhance language model metacognition and achieve faithful calibration.
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Papers
cs.CLcs.AIEmpiricalRecentJun 30, 2026
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
This paper introduces two novel mechanisms, reinforcement learning with metacognitive feedback (RLMF) and metacognitive data selection, to enhance language model metacognition and achieve faithful cal…
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cs.CVcs.AIcs.CLRecent