1 citations · 1 across the 17 of their papers we have counts for
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SATER: A Self-Aware and Token-Efficient Approach to Routing and Cascading
Yuanzhe Shen, Yide Liu, Zisu Huang +3
Large language models (LLMs) demonstrate remarkable performance across diverse tasks, yet their effectiveness frequently depends on costly commercial APIs or cloud services. Model…
IntentionReasoner: Facilitating Adaptive LLM Safeguards through Intent Reasoning and Selective Query Refinement
Yuanzhe Shen, Zisu Huang, Zhengkang Guo +5
The rapid advancement of large language models (LLMs) has driven their adoption across diverse domains, yet their ability to generate harmful content poses significant safety chall…
Enhancing Model Privacy in Federated Learning with Random Masking and Quantization
Zhibo Xu, Jianhao Zhu, Jingwen Xu +7
The primary goal of traditional federated learning is to protect data privacy by enabling distributed edge devices to collaboratively train a shared global model while keeping raw…
Progressive Mastery: Customized Curriculum Learning with Guided Prompting for Mathematical Reasoning
Muling Wu, Qi Qian, Wenhao Liu +12
Large Language Models (LLMs) have achieved remarkable performance across various reasoning tasks, yet post-training is constrained by inefficient sample utilization and inflexible…
RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data
Zhengkang Guo, Wenhao Liu, Mingchen Xie +13
Large language models (LLMs) are increasingly expected to tackle complex tasks, driven by their expanding applications and users' growing proficiency in crafting sophisticated prom…