40 citations · 137 across the 14 of their papers we have counts for
8 papers · 1 filter
ORCE: Order-Aware Alignment of Verbalized Confidence in Large Language Models
Chen Li, Xiaoling Hu, Songzhu Zheng +2
Large language models (LLMs) often produce answers with high certainty even when they are incorrect, making reliable confidence estimation essential for deployment in real-world sc…
SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents
Xiwen Chen, Wenhui Zhu, Songzhu Zheng +3
Large language models (LLMs) are increasingly deployed for autonomous financial trading, a domain requiring continuous adaptation to noisy, non-stationary markets. Existing self-im…
Reweighting Improves Conditional Risk Bounds
Yikai Zhang, Jiahe Lin, Fengpei Li +4
In this work, we study the weighted empirical risk minimization (weighted ERM) schema, in which an additional data-dependent weight function is incorporated when the empirical risk…
Attention-Enhancing Backdoor Attacks Against BERT-based Models
Weimin Lyu, Songzhu Zheng, Lu Pang +2
Recent studies have revealed that \textit{Backdoor Attacks} can threaten the safety of natural language processing (NLP) models. Investigating the strategies of backdoor attacks wi…
Learning to Abstain From Uninformative Data
Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard +5
Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high. In this pape…
Attention Hijacking in Trojan Transformers
Weimin Lyu, Songzhu Zheng, Tengfei Ma +2
Trojan attacks pose a severe threat to AI systems. Recent works on Transformer models received explosive popularity and the self-attentions are now indisputable. This raises a cent…
Topological Detection of Trojaned Neural Networks
Songzhu Zheng, Yikai Zhang, Hubert Wagner +2
Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through…
Learning with Feature-Dependent Label Noise: A Progressive Approach
Yikai Zhang, Songzhu Zheng, Pengxiang Wu +2
Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing…