arXiv:2607.08377v1 Announce Type: new Abstract: Uncertainty quantification is central to the reliable deployment of large language models (LLMs), and eigenvalues of semantic embeddings have recently emerged as a key tool in state-of-the-art methods. However, conventional calibration results developed for classification probabilities cannot be directly transferred to eigenvalues. We address this gap by proposing a novel framework for calibrating the eigenvalues of semantic embeddings. We interpret LLMs combined with semantic embeddings of their generated answers as density matrix predictors, an
The increasing deployment of LLMs into critical applications necessitates robust uncertainty quantification, driving research into methods like eigenvalue calibration for semantic embeddings.
Improved calibration of LLM uncertainty directly impacts their reliability and trustworthiness, crucial for their integration into sensitive and high-stakes systems.
This research provides a novel framework for quantifying and calibrating LLM uncertainty using eigenvalues, moving beyond conventional classification probability methods.
- · AI developers
- · LLM application users
- · Sectors requiring high AI reliability (e.g., finance, healthcare)
- · Developers of unreliable LLM applications
- · Methods for uncertainty quantification based solely on classification probabilit
Increased reliability and trust in Large Language Models as uncertainty quantification improves.
Faster adoption of LLMs in regulated industries due to better-understood risk profiles.
The development of new regulatory frameworks for AI that incorporate advanced uncertainty quantification metrics.
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Read at arXiv cs.LG