Publications (13)
CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models
Xuechen Liang, Yangfan He, Meiling Tao +5
Open large language models (LLMs) have significantly advanced the field of natural language processing, showcasing impressive performance across various tasks.Despite the significa…
OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use
Xueyu Hu, Tao Xiong, Biao Yi +26
The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of (multi-modal) large la…
MiCoTA: Bridging the Learnability Gap with Intermediate CoT and Teacher Assistants
Dongyi Ding, Tiannan Wang, Chenghao Zhu +3
Large language models (LLMs) excel at reasoning tasks requiring long thought sequences for planning, reflection, and refinement. However, their substantial model size and high comp…
PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalization
Meiling Tao, Chenghao Zhu, Dongyi Ding +3
With the rapid improvement in the general capabilities of LLMs, LLM personalization, i.e., how to build LLM systems that can generate personalized responses or services that are ta…
Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentary
Meiling Tao, Xuechen Liang, Xinyuan Song +4
Recent advancements in large language models (LLMs) have unlocked the potential for generating high-quality game commentary. However, producing insightful and engaging commentary f…
AI PERSONA: Towards Life-long Personalization of LLMs
Tiannan Wang, Meiling Tao, Ruoyu Fang +4
In this work, we introduce the task of life-long personalization of large language models. While recent mainstream efforts in the LLM community mainly focus on scaling data and com…