1 citations · 1 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
Xmodel-1.5: An 1B-scale Multilingual LLM
Wang Qun, Liu Yang, Lin Qingquan +1
We introduce Xmodel-1.5, a 1-billion-parameter multilingual large language model pretrained on 2 trillion tokens, designed for balanced performance and scalability. Unlike most lar…