arXiv:2510.05566v2 Announce Type: replace-cross Abstract: Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real-world applications. Conformal prediction provides finite-sample, distribution-free coverage guarantees, but standard conformal prediction breaks down under domain shift, often leading to under-coverage and unreliable prediction sets. We propose a new framework called Domain-Shift-Aware Conformal Prediction (DS-CP). Our framework ada
The increasing deployment of large language models in critical applications highlights the urgent need to address their inherent unreliability and hallucinatory tendencies, especially in dynamic real-world scenarios.
Improving the robustness and trustworthiness of AI, particularly LLMs, is crucial for widespread adoption and reliance in sectors demanding high accuracy and safety, mitigating risks associated with overconfident incorrect outputs.
This research introduces a method to maintain reliability guarantees for LLMs even when their operational environment deviates from training data, potentially broadening safe deployment in complex, evolving domains.
- · AI developers
- · Enterprise AI adopters
- · High-stakes application sectors (e.g., finance, healthcare)
- · AI systems lacking robustness mechanisms
- · Organizations deploying unreliable AI systems
Increased trust and accelerated adoption of LLMs in production environments requiring high assurance.
Differentiation among AI providers based on the reliability and safety guarantees of their models.
Potential for new regulatory standards or certifications to emerge around domain-shift robustness in AI.
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