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为代表的指令遵循大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语言模型构成了显著挑战。亟需一款专为电商场景定制、具备跨数据集/任务强泛化能力的大语言模型。为此，本工作首次提出面向电商的指令数据集EcomInstruct，共包含250万条指令数据。通过基于电商基础数据类型（如商品信息、用户评论）构建原子任务，EcomInstruct在数据规模和任务多样性上实现了扩展。原子任务定义为隐式参与最终任务求解的中间任务，我们亦将其称为任务链任务。我们以BLOOMZ为骨干模型，利用EcomInstruct进行训练，开发了不同参数规模的EcomGPT。得益于任务链任务所习得的基础语义理解能力，EcomGPT展现出优异的零样本泛化性能。大量实验和人工评估表明，在电商任务上，EcomGPT在跨数据集/任务泛化方面优于ChatGPT。