arXiv:2606.09875v1 Announce Type: new Abstract: Large language models hallucinate confidently, making uncertainty quantification (UQ) essential for reliable deployment. Existing methods rely predominantly on token-level signals, leaving the geometric structure of intermediate hidden states underused. In this paper, we take the geometric complexity of hidden-state matrices as a measure of the global uncertainty of LLMs, while treating token-level uncertainty estimation as a local metric. We show that hidden-state geometric entropy (global uncertainty) and token-level entropy (local uncertainty)
The rapid deployment and increasing reliance on large language models necessitate robust uncertainty quantification to build trust and ensure safety in AI applications.
This research provides a novel method for more comprehensive uncertainty quantification in LLMs, which is critical for their reliable integration into sensitive and mission-critical systems.
The proposed approach of integrating local and global entropy offers a more nuanced understanding of LLM uncertainty, moving beyond solely token-level analysis.
- · AI safety researchers
- · Developers of foundational models
- · Industries deploying LLMs in high-stakes environments
- · Developers of unreliable LLM solutions
Improved methods for auditing and validating LLM outputs become available.
Increased trust in AI systems could accelerate their adoption in regulated sectors.
New regulatory frameworks may emerge, incorporating multi-faceted uncertainty metrics as requirements for AI deployment.
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Read at arXiv cs.LG