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Recently, instruction-following Large Language Models (LLMs) , represented byChatGPT, have exhibited exceptional performance in general Natural LanguageProcessing (NLP) tasks. However, the unique characteristics of E-commerce datapose significant challenges to general LLMs. An LLM tailored specifically forE-commerce scenarios, possessing robust cross-dataset/task generalizationcapabilities, is a pressing necessity. To solve this issue, in this work, weproposed the first e-commerce instruction dataset EcomInstruct, with a total of2.5 million instruction data. EcomInstruct scales up the data size and taskdiversity by constructing atomic tasks with E-commerce basic data types, suchas product information, user reviews. Atomic tasks are defined as intermediatetasks implicitly involved in solving a final task, which we also callChain-of-Task tasks. We developed EcomGPT with different parameter scales bytraining the backbone model BLOOMZ with the EcomInstruct. Benefiting from thefundamental semantic understanding capabilities acquired from the Chain-of-Tasktasks, EcomGPT exhibits excellent zero-shot generalization capabilities.Extensive experiments and human evaluations demonstrate that EcomGPToutperforms ChatGPT in term of cross-dataset/task generalization on E-commercetasks.

EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce