fine-tuning 1large language models 1risk detection 1safety assessment 1semantic analysis 1subspace alignment 1
From the 1 of 2 linked papers with an AI index.
2 papers
cs.AI2026
REIN: Bridging the Gap between Reasoning and Reliability via Reflection and Abstention Alignment
Zhengze Huang, Luyang Yu, Di Hong +5
Large reasoning models (LRMs) are prone to hallucination, which undermines their reliability and poses challenges for safe deployment. Hallucinations in LRMs arise from two distinc…
cs.CR2026
DataShield: Uncovering Risky Fine-Tuning Data Across LLMs Through Consensus Subspace Alignment
Zefeng Wu, Weiwei Qi, Jielong Chen +6
The paper introduces DataShield, a framework that detects risky fine‑tuning data for large language models by aligning safety‑critical semantic subspaces across multiple safety‑ali…