3 citations · 5 across the 6 of their papers we have counts for
4 papers · 1 filter
Learning to Seek Help: Dynamic Collaboration Between Small and Large Language Models
Hang Zeng, Xiangyu Liu, Yong Hu +5
Large language models (LLMs) offer strong capabilities but raise cost and privacy concerns, whereas small language models (SLMs) facilitate efficient and private local inference ye…
From Myopic Selection to Long-Horizon Awareness: Sequential LLM Routing for Multi-Turn Dialogue
Jiarui Zhang, Xiangyu Liu, Yong Hu +5
Multi-turn dialogue is the predominant form of interaction with large language models (LLMs). While LLM routing is effective in single-turn settings, existing methods fail to maxim…
Automated Privacy Information Annotation in Large Language Model Interactions
Hang Zeng, Xiangyu Liu, Yong Hu +4
Users interacting with large language models (LLMs) under their real identifiers often unknowingly risk disclosing private information. Automatically notifying users whether their…
Personalized LLM for Generating Customized Responses to the Same Query from Different Users
Hang Zeng, Chaoyue Niu, Fan Wu +2
Existing work on large language model (LLM) personalization assigned different responding roles to LLMs, but overlooked the diversity of queriers. In this work, we propose a new fo…