6 papers
Xmodel-2.5: 1.3B Data-Efficient Reasoning SLM
Yang Liu, Xiaolong Zhong, Ling Jiang
Large language models deliver strong reasoning and tool-use skills, yet their computational demands make them impractical for edge or cost-sensitive deployments. We present \textbf…
ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues?
Haoxin Wang, Xianhan Peng, Xucheng Huang +5
In this paper, we introduce ECom-Bench, the first benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain. ECom-Bench fe…
MemOrb: A Plug-and-Play Verbal-Reinforcement Memory Layer for E-Commerce Customer Service
Yizhe Huang, Yang Liu, Ruiyu Zhao +3
Large Language Model-based agents(LLM-based agents) are increasingly deployed in customer service, yet they often forget across sessions, repeat errors, and lack mechanisms for con…
Survey of Specialized Large Language Model
Chenghan Yang, Ruiyu Zhao, Yang Liu +1
The rapid evolution of specialized large language models (LLMs) has transitioned from simple domain adaptation to sophisticated native architectures, marking a paradigm shift in AI…
MindFlow: Revolutionizing E-commerce Customer Support with Multimodal LLM Agents
Ming Gong, Xucheng Huang, Chenghan Yang +4
Recent advances in large language models (LLMs) have enabled new applications in e-commerce customer service. However, their capabilities remain constrained in complex, multimodal…
Xmodel-LM Technical Report
Yichuan Wang, Yang Liu, Yu Yan +3
We introduce Xmodel-LM, a compact and efficient 1.1B language model pre-trained on around 2 trillion tokens. Trained on our self-built dataset (Xdata), which balances Chinese and E…