A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models
arXiv:2606.19868v1 Announce Type: new Abstract: Although large language models (LLMs) have shown strong capabilities across a wide range of tasks, their outputs often remain unreliable and may contain hallucinations, making uncertainty estimation (UE) essential for building trustworthy LLMs. In practice, many mainstream LLMs are only accessible through restricted APIs, where internal signals such as logits and hidden states are unavailable, making black-box UE especially important. However, existing work on black-box UE for LLMs remains fragmented in methodology and lacks a unified empirical c
The proliferation of black-box LLMs necessitates robust uncertainty estimation methods to maintain trustworthiness and reliability as these models are integrated into critical applications.
Ensuring the reliability of LLMs, especially those behind restricted APIs, is crucial for widespread adoption and for mitigating the risks associated with hallucinations and erroneous outputs.
This research provides a more systematic approach to evaluating black-box uncertainty estimation, potentially leading to more trustworthy and deployable LLM applications that can signal their own limitations.
- · LLM developers
- · AI safety researchers
- · Enterprises deploying LLMs
- · Untrustworthy LLM applications
- · Users relying on unvalidated LLM outputs
Improved methods for LLM uncertainty estimation become standard practice.
Increased confidence in LLM deployments across sensitive sectors like finance and healthcare.
Enhanced regulatory frameworks for AI systems, requiring auditable uncertainty metrics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI