eCeLLM: Generalizing Large Language Models for E-commerce from
  Large-scale, High-quality Instruction Data
Bo Peng Xinyi Ling Ziru Chen Huan Sun Xia Ning
With tremendous efforts on developing effective e-commerce models,
conventional e-commerce models show limited success in generalist e-commerce
modeling, and suffer from unsatisfactory performance on new users and new
products - a typical out-of-domain generalization challenge. Meanwhile, large
language models (LLMs) demonstrate outstanding performance in generalist
modeling and out-of-domain generalizability in many fields. Toward fully
unleashing their power for e-commerce, in this paper, we construct ECInstruct,
the first open-sourced, large-scale, and high-quality benchmark instruction
dataset for e-commerce. Leveraging ECInstruct, we develop eCeLLM, a series of
e-commerce LLMs, by instruction-tuning general-purpose LLMs. Our comprehensive
experiments and evaluation demonstrate that eCeLLM models substantially
outperform baseline models, including the most advanced GPT-4, and the
state-of-the-art task-specific models in in-domain evaluation. Moreover, eCeLLM
exhibits excellent generalizability to out-of-domain settings, including unseen
products and unseen instructions, highlighting its superiority as a generalist
e-commerce model. Both the ECInstruct dataset and the eCeLLM models show great
potential in empowering versatile and effective LLMs for e-commerce. ECInstruct
and eCeLLM models are publicly accessible through
https://ninglab.github.io/eCeLLM.