arXiv:2605.28170v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly integrated into high-stakes decision-making, the ability to reliably quantify uncertainty has become a critical requirement for safety and trust. However, current uncertainty quantification methods primarily operate at the output level, often failing to distinguish whether uncertainty arises from the model's lack of knowledge or from ambiguity in the user's input. While input-centric uncertainty quantification has recently emerged as a promising direction, it remains relatively underexplored and ty
The increasing integration of LLMs into high-stakes decision-making drives a critical need for advanced uncertainty quantification methods, pushing research into novel areas like input-centric analysis.
Reliable uncertainty quantification, particularly distinguishing between model knowledge gaps and input ambiguity, is crucial for fostering trust, ensuring safety, and expanding the responsible deployment of LLMs in sensitive applications.
This research shifts the focus from solely output-level uncertainty to include input-centric analysis, offering a more granular understanding of why an LLM might be uncertain and improving interpretability.
- · LLM developers
- · Organizations deploying LLMs in high-stakes environments
- · AI safety researchers
- · Users of LLM-powered applications
- · LLM applications with opaque uncertainty handling
- · Development teams unequipped to implement advanced UQ methods
Improved trust and adoption of LLMs in critical sectors due to enhanced transparency regarding their limitations.
New standards and regulations emerging for LLM uncertainty quantification, potentially influencing model development and deployment pipelines.
The development of 'uncertainty-aware' LLM architectures that can dynamically query for clarifying input or adjust confidence based on localized ambiguities.
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Read at arXiv cs.AI