eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
<|reference_start|>eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data: 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.<|reference_end|>
@article{peng2024ecellm:,
    title={eCeLLM: Generalizing Large Language Models for E-commerce from
  Large-scale, High-quality Instruction Data},
    author={Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, Xia Ning},
    journal={arXiv preprint arXiv:2402.08831},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08831},
    primaryClass={cs.CL cs.AI cs.IR}
}