Research Paper#Large Language Models, Conformal Prediction, Uncertainty Quantification🔬 ResearchAnalyzed: Jan 3, 2026 16:22
Conformal Prediction for LLM Next-Token Prediction
Published:Dec 27, 2025 19:08
•1 min read
•ArXiv
Analysis
This paper addresses the critical need for uncertainty quantification in large language models (LLMs), particularly in high-stakes applications. It highlights the limitations of standard softmax probabilities and proposes a novel approach, Vocabulary-Aware Conformal Prediction (VACP), to improve the informativeness of prediction sets while maintaining coverage guarantees. The core contribution lies in balancing coverage accuracy with prediction set efficiency, a crucial aspect for practical deployment. The paper's focus on a practical problem and the demonstration of significant improvements in set size make it valuable.
Key Takeaways
- •Addresses the problem of poorly calibrated probabilities in LLMs.
- •Proposes Vocabulary-Aware Conformal Prediction (VACP) to improve prediction set efficiency.
- •Demonstrates significant reduction in prediction set size while maintaining coverage guarantees.
- •Provides a practical solution for uncertainty quantification in LLMs.
Reference
“VACP achieves 89.7 percent empirical coverage (90 percent target) while reducing the mean prediction set size from 847 tokens to 4.3 tokens -- a 197x improvement in efficiency.”