4 citations · 16 across the 19 of their papers we have counts for
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Quantifying and Understanding Uncertainty in Large Reasoning Models
Yangyi Li, Chenxu Zhao, Mengdi Huai
Large Reasoning Models (LRMs) have recently demonstrated significant improvements in complex reasoning. While quantifying generation uncertainty in LRMs is crucial, traditional met…
Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
Aobo Chen, Chenxu Zhao, Chenglin Miao +1
Large language models (LLMs) possess strong semantic understanding, driving significant progress in data mining applications. This is further enhanced by large reasoning models (LR…
Selective Forgetting for Large Reasoning Models
Tuan Le, Wei Qian, Mengdi Huai
Large Reasoning Models (LRMs) generate structured chains of thought (CoTs) before producing final answers, making them especially vulnerable to knowledge leakage through intermedia…
Uncertainty-aware Language Guidance for Concept Bottleneck Models
Yangyi Li, Mengdi Huai
Concept Bottleneck Models (CBMs) provide inherent interpretability by first mapping input samples to high-level semantic concepts, followed by a combination of these concepts for t…