3 citations · 5 across the 4 of their papers we have counts for
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cs.LG2025
LoRA-BAM: Input Filtering for Fine-tuned LLMs via Boxed Abstraction Monitors over LoRA Layers
Changshun Wu, Tianyi Duan, Saddek Bensalem +1
Fine-tuning large language models (LLMs) improves performance on domain-specific tasks but can lead to overfitting, making them unreliable on out-of-distribution (OoD) queries. We…
cs.LG2020★ 3 cited
Active Deep Learning on Entity Resolution by Risk Sampling
Youcef Nafa, Qun Chen, Zhaoqiang Chen +4
While the state-of-the-art performance on entity resolution (ER) has been achieved by deep learning, its effectiveness depends on large quantities of accurately labeled training da…
cs.LG2020★ 2 cited
Adaptive Deep Learning for Entity Resolution by Risk Analysis
Zhaoqiang Chen, Qun Chen, Youcef Nafa +4
The state-of-the-art performance on entity resolution (ER) has been achieved by deep learning. However, deep models are usually trained on large quantities of accurately labeled tr…