Beyond Surface Statistics: Robust Conformal Prediction for LLMs via Internal Representations
Summary: arXiv:2604.16217v1 Announce Type: cross
Large language models (LLMs) are becoming integral in various applications, especially in domains where the reliability of output is crucial. However, the uncertainty signals typically derived from output-level statistics—such as token probabilities, entropy, and self-consistency—often exhibit instability when subjected to calibration-deployment mismatches. This raises the need for more robust methods to ensure the reliability of these models in real-world applications.
Introduction to Conformal Prediction
Conformal prediction stands out as a method that ensures finite-sample validity under the assumption of exchangeability. This approach allows for the generation of valid predictive intervals and sets, offering significant advantages in uncertainty quantification. However, the practical application of conformal prediction heavily relies on the quality of the nonconformity score employed. Traditional methods often rely on surface-level statistics, which can be unreliable in various scenarios.
Proposed Framework
In this context, we introduce a novel conformal framework designed specifically for question answering tasks involving LLMs. Our method utilizes internal representations rather than merely output-facing statistics. We present a new metric known as Layer-Wise Information (LI) scores. These scores quantify how conditioning on the input modifies the predictive entropy at different depths within the model, thus serving as effective nonconformity scores in a standard split conformal pipeline.
Methodology and Results
The framework operates by leveraging the internal dynamics of the LLM, which allows for a more nuanced understanding of uncertainty. We evaluated the proposed method across various benchmarks, including both closed-ended and open-domain question answering tasks. Notably, our framework demonstrated significant improvements in situations characterized by cross-domain shifts, where traditional methods often falter.
- Validity and Efficiency: Our approach achieves a superior trade-off between validity and efficiency compared to strong text-level baseline methods.
- In-Domain Reliability: The method maintains competitive reliability in in-domain scenarios while adhering to the same nominal risk levels as conventional models.
- Cross-Domain Performance: The results highlight the effectiveness of using internal representations for generating conformal scores, especially when surface-level uncertainty is prone to instability under distribution shifts.
Conclusion
The findings suggest that internal representations within large language models can provide a more informative basis for conformal prediction, particularly in contexts where surface-level uncertainty may not accurately reflect the model’s reliability. As LLMs continue to be deployed in critical applications, this research paves the way for more robust frameworks that enhance the reliability and interpretability of model predictions.
Future Work
Future research could explore the integration of additional internal metrics and investigate their collective impact on conformal prediction methodologies. Additionally, expanding the framework to accommodate various types of LLM architectures could yield even more robust and versatile predictive capabilities.