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Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence?

arXiv — cs.CL•Thursday, May 28, 2026 at 4:00:00 AM
NeutralArtificial Intelligence
  • What Happened

    A recent study investigates whether large language models (LLMs) can effectively use linguistic uncertainty markers to reflect their intrinsic confidence levels. The research formalizes the concept of marker internal confidence (MIC) and evaluates LLMs' ability to associate specific markers with confidence levels across various tasks, revealing persistent miscalibration in their responses.

  • Why It Matters

    This development is significant as it highlights the limitations of LLMs in accurately conveying confidence, which is crucial for applications requiring reliable decision-making and communication.

  • The Bigger Picture

    The findings resonate with ongoing discussions about the reliability of LLMs in various domains, including their adherence to Bayesian principles and the challenges of aligning their outputs with human expectations, underscoring the need for improved calibration methods in AI systems.

— via World Pulse Now AI Editorial System
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