Recently, instruction-following Large Language Models (LLMs) , represented by ChatGPT, have exhibited exceptional performance in general Natural Language Processing (NLP) tasks. However, the unique characteristics of E-commerce data pose significant challenges to general LLMs. An LLM tailored specifically for E-commerce scenarios, possessing robust cross-dataset/task generalization capabilities, is a pressing necessity. To solve this issue, in this work, we proposed the first e-commerce instruction dataset EcomInstruct, with a total of 2.5 million instruction data. EcomInstruct scales up the data size and task diversity by constructing atomic tasks with E-commerce basic data types, such as product information, user reviews. Atomic tasks are defined as intermediate tasks implicitly involved in solving a final task, which we also call Chain-of-Task tasks. We developed EcomGPT with different parameter scales by training the backbone model BLOOMZ with the EcomInstruct. Benefiting from the fundamental semantic understanding capabilities acquired from the Chain-of-Task tasks, EcomGPT exhibits excellent zero-shot generalization capabilities. Extensive experiments and human evaluations demonstrate that EcomGPT outperforms ChatGPT in term of cross-dataset/task generalization on E-commerce tasks.

翻译：近期，以ChatGPT为代表的指令跟随型大型语言模型在通用自然语言处理任务中展现出卓越性能。然而，电商数据的独特性对通用大型语言模型构成了显著挑战。针对电商场景专门定制、具备强大跨数据集/任务泛化能力的语言模型已成为迫切需求。为解决此问题，本文首次提出电商指令数据集EcomInstruct，包含总计250万条指令数据。该数据集通过构建基于电商基础数据类型（如商品信息、用户评论）的原子任务，实现了数据规模与任务多样性的扩展。原子任务被定义为隐含在解决最终任务过程中的中间任务，我们将其称为"任务链"任务。通过使用EcomInstruct对骨干模型BLOOMZ进行训练，我们开发了不同参数规模的EcomGPT。得益于从任务链任务中获得的基础语义理解能力，EcomGPT展现出卓越的零样本泛化能力。大量实验与人工评估结果表明，在电商任务的跨数据集/任务泛化方面，EcomGPT性能优于ChatGPT。