1 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection
Shuhao Chen, Weisen Jiang, Yeqi Gong +5
Fine-tuning large language models often undermines their safety alignment, a problem further amplified by harmful fine-tuning attacks in which adversarial data removes safeguards a…
RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation
Shuhao Chen, Weisen Jiang, Changmiao Wang +4
Inpatient medication recommendation requires clinicians to repeatedly select specific medications, doses, and routes as a patient's condition evolves. Existing benchmarks formulate…
MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification
Weisen Jiang, Shuhao Chen, Sinno Jialin Pan
Mixture-of-Experts (MoE) models scale capacity by combining specialized experts, but most existing approaches assume centralized access to training data. In practice, data are dist…
Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation
Zhan Zhuang, Xiequn Wang, Wei Li +9
Low-rank adaptation (LoRA) has emerged as a leading parameter-efficient fine-tuning technique for adapting large foundation models, yet it often locks adapters into suboptimal mini…
RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models
Shuhao Chen, Weisen Jiang, Baijiong Lin +2
Recent works show that assembling multiple off-the-shelf large language models (LLMs) can harness their complementary abilities. To achieve this, routing is a promising method, whi…