18 citations · 23 across the 10 of their papers we have counts for
9 papers · 1 filter
TEXAS: Task-Expert-Aware Supervision for Downstream Mixture-of-Experts LLM Adaptation
Guanzhi Deng, Haibo Wang, Kuan Wu +5
Mixture-of-Experts (MoE) language models route each token through a small subset of experts, making routing patterns useful for identifying task-relevant experts during downstream…
Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Models
Guanzhi Deng, Kuan Wu, Haibo Wang +3
Mixture-of-Experts (MoE) models have emerged as a dominant paradigm for efficient LLM scaling, yet adapting them to non-English downstream tasks remains challenging. Existing fine-…
Reasoning Meets Personalization: Unleashing the Potential of Large Reasoning Model for Personalized Generation
Sichun Luo, Guanzhi Deng, Jian Xu +4
Personalization is a critical task in modern intelligent systems, with applications spanning diverse domains, including interactions with large language models (LLMs). Recent advan…
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling
Yuxuan Yao, Han Wu, Mingyang Liu +5
Large language models (LLMs) exhibit varying strengths and weaknesses across different tasks, prompting recent studies to explore the benefits of ensembling models to leverage thei…
Privacy in LLM-based Recommendation: Recent Advances and Future Directions
Sichun Luo, Wei Shao, Yuxuan Yao +9
Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance. However, while most of the existing works…
Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
Yuxuan Yao, Sichun Luo, Haohan Zhao +2
We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{…