3 papers
cs.LG2026
Unlocking the Pre-Trained Model as a Dual-Alignment Calibrator for Post-Trained LLMs
Beier Luo, Cheng Wang, Hongxin Wei +2
Post-training improves large language models (LLMs) but often worsens confidence calibration, leading to systematic overconfidence. Recent unsupervised post-hoc methods for post-tr…
cs.CR2025
Dataset Ownership in the Era of Large Language Models
Kun Li, Cheng Wang, Minghui Xu +2
As datasets become critical assets in modern machine learning systems, ensuring robust copyright protection has emerged as an urgent challenge. Traditional legal mechanisms often f…
cs.CL2024
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models
Youan Cong, Pritom Saha Akash, Cheng Wang +1
We introduce the \textit{Extract-Refine-Retrieve-Read} (ERRR) framework, a novel approach designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RA…