Why Don't You Know? Evaluating the Impact of Uncertainty Sources on Uncertainty Quantification in LLMs
arXiv:2604.10495v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed in real-world applications, reliable uncertainty quantification (UQ) becomes critical for safe and effective use. Most existing UQ approaches for language models aim to produce a single confidence score -- for example, estimating the probability that a model's answer is correct. However, uncertainty in natural language tasks arises from multiple distinct sources, including model knowledge gaps, output variability, and input ambiguity, which have different implications for system behavi
As LLMs move from research to critical real-world applications, the need for robust and transparent uncertainty quantification becomes paramount for safety and reliability.
Understanding the distinct sources of LLM uncertainty allows for more targeted mitigation strategies, improving trust and operational efficacy in high-stakes deployments.
The focus shifts from a single confidence score to a nuanced understanding of multiple uncertainty sources (knowledge gaps, output variability, input ambiguity), enabling more sophisticated error analysis and model development.
- · AI developers
- · High-stakes application industries (e.g., healthcare, finance)
- · LLM safety and alignment researchers
- · LLM deployments without robust UQ
- · Systems relying solely on single-score confidence metrics
More reliable and interpretable LLM outputs in critical applications.
Accelerated adoption of LLMs in regulated sectors due to increased trustworthiness.
Development of new LLM architectures specifically designed with inherent, multi-faceted uncertainty quantification capabilities.
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Read at arXiv cs.CL