2 papers
cs.CL2025
R-Capsule: Compressing High-Level Plans for Efficient Large Language Model Reasoning
Hongyu Shan, Mingyang Song, Chang Dai +2
Chain-of-Thought (CoT) prompting helps Large Language Models (LLMs) tackle complex reasoning by eliciting explicit step-by-step rationales. However, CoT's verbosity increases laten…
cs.CL2025
COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation
Sean Wang, Yicheng Jiang, Yuxin Tang +2
Uncertainty Quantification (UQ) for Natural Language Generation (NLG) is crucial for assessing the performance of Large Language Models (LLMs), as it reveals confidence in predicti…