uncertainty-quantification

You can calibrate LLM confidence by applying tiered sampling with semantic entropy and consistency checks to produce an uncertainty score. This score generates a proceed, proceed_with_caveat, abstain, or escalate recommendation to gate agent actions.

Measuring LLM uncertainty in a black-box retrieval system is achievable using SelfCheckGPT-style consistency checks and conformal prediction. These techniques estimate semantic entropy without requiring access to internal logprobs, enabling selective retrieval decisions.

You use conformal prediction and semantic entropy within a tiered sampling protocol to evaluate response consistency. This produces actionable signals that help your RAG system choose between answering directly, retrieving more context, or abstaining entirely.

Yes, you can use logprob-based uncertainty estimation alongside black-box consistency checks. The approach supports both white-box and black-box settings, applying logprob methods when available and falling back to black-box estimators otherwise.

You should escalate to a human when an action is irreversible and uncertainty is high. The uncertainty quantification protocol outputs an explicit escalate recommendation when high-stakes factuality checks fail to produce reliable confidence.

A limitation of using semantic entropy for agent safety is the computational cost of tiered sampling. It requires multiple generation passes to estimate consistency, which may introduce latency before the system can safely gate writes, deletes, or deployments.