From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning

2025-04-22

AI Summary

This work addresses the lack of statistical guarantees for uncertainty quantification in large language model (LLM) in-context learning (ICL). We propose ICL-CP—the first framework to integrate distribution-free conformal prediction with ICL, enabling calibrated prediction intervals via a single forward pass. Unlike conventional conformal methods requiring repeated retraining, ICL-CP bypasses this computational bottleneck while establishing theoretical foundations for scalability and distribution-shift robustness in ICL-based uncertainty estimation. Empirically, on noisy regression tasks, ICL-CP achieves ≈90% empirical coverage for 90%-confidence intervals—substantially outperforming the ridge-conformal baseline—and supports real-time interval prediction even for hundred-layer-parameter models.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: (Large) Language Models
• Reasoning under Uncertainty: Probabilistic Programming

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Transformers have become a standard architecture in machine learning, demonstrating strong in-context learning (ICL) abilities that allow them to learn from the prompt at inference time. However, uncertainty quantification for ICL remains an open challenge, particularly in noisy regression tasks. This paper investigates whether ICL can be leveraged for distribution-free uncertainty estimation, proposing a method based on conformal prediction to construct prediction intervals with guaranteed coverage. While traditional conformal methods are computationally expensive due to repeated model fitting, we exploit ICL to efficiently generate confidence intervals in a single forward pass. Our empirical analysis compares this approach against ridge regression-based conformal methods, showing that conformal prediction with in-context learning (CP with ICL) achieves robust and scalable uncertainty estimates. Additionally, we evaluate its performance under distribution shifts and establish scaling laws to guide model training. These findings bridge ICL and conformal prediction, providing a theoretically grounded and new framework for uncertainty quantification in transformer-based models.

Problem

• Uncertainty quantification for in-context learning in noisy regression tasks
• Leveraging conformal prediction for distribution-free uncertainty estimation
• Efficiently generating confidence intervals in a single forward pass

Innovation

• Conformal prediction for uncertainty estimation
• Single-pass confidence intervals via ICL
• Robust scaling laws for model training