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

• URL: http://arxiv.org/abs/2308.06966v2
• Date: Mon, 28 Aug 2023 04:12:30 GMT
• Title: EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
• Authors: Yangning Li, Shirong Ma, Xiaobin Wang, Shen Huang, Chengyue Jiang, Hai-Tao Zheng, Pengjun Xie, Fei Huang, Yong Jiang
• Abstract summary: We propose the first e-commerce instruction dataset EcomInstruct, with a total of 2.5 million instruction data. EcomGPT outperforms ChatGPT in term of cross-dataset/task generalization on E-commerce tasks.
• Score: 68.72104414369635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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