Beyond Surface Statistics: Robust Conformal Prediction for LLMs via Internal Representations

Researchers propose a conformal prediction framework for large language models that uses internal neural representations rather than surface-level outputs to assess reliability and uncertainty. The Layer-Wise Information scoring method improves prediction validity under distribution shift while maintaining competitive performance, addressing a critical challenge in deploying LLMs where traditional uncertainty signals become unreliable.

This research addresses a fundamental problem in LLM deployment: surface-level uncertainty metrics like token probabilities and entropy become unreliable when training and deployment conditions diverge. The proposed Layer-Wise Information scoring approach examines how input conditioning reshapes predictive entropy across model layers, extracting more stable uncertainty signals from internal network dynamics rather than relying on brittle output statistics.

The work builds on conformal prediction theory, which guarantees finite-sample validity under exchangeability assumptions. However, conformal prediction's effectiveness depends entirely on nonconformity scores—a quality bottleneck this research directly targets. By probing internal representations, the method captures genuine model uncertainty tied to learned feature processing rather than surface artifacts.

For practitioners deploying LLMs in high-stakes applications—medical diagnosis, financial analysis, legal document review—robust uncertainty quantification directly translates to better risk management and decision confidence. The method demonstrates particular strength under cross-domain shift, a common real-world scenario where models encounter data distributions unlike their training set. This capability addresses a major deployment pain point.

The findings suggest that model internals contain richer uncertainty information than outputs reveal, opening avenues for more sophisticated reliability frameworks. As LLMs integrate into critical infrastructure, methods that improve reliability under distribution shift become economically valuable. Future work should explore whether these internal signals generalize across model architectures and scales, and whether they enable better uncertainty decomposition for mixture-of-experts or retrieval-augmented systems.

• →Internal neural representations provide more stable uncertainty signals than output-level metrics under distribution shift
• →Layer-Wise Information scores improve the validity-efficiency trade-off in conformal prediction for question-answering tasks
• →The method maintains competitive in-domain performance while excelling under cross-domain distribution shifts
• →Conformal prediction frameworks depend critically on nonconformity score quality, which internal representations address
• →Research suggests a fundamental advantage to probing model internals rather than surface outputs for reliability assessment