Abstract: The commodity attribute classification task refers to the process of analyzing the attributes of a piece of merchandise based on its descriptive text and subsequently categorizing multiple attributes.This process aids in providing insights into merchandise from various perspectives,thereby assisting in marketing and product management.While the utilization of large language models is increasingly prevalent,their performance in commodity attribute classification tasks remains suboptimal due to the lack of domain knowledge and attribute correlations.To address this issue,this paper proposes a dual pre-training-based method for commodity attribute classification,aiming to enhance the performance of large language models in such tasks by employing specific pre-training techniques.Building upon the T5 model,this paper introduces two methods:domain-specific text pre-training and attribute correlation-based pre-training.These methods enhance the model's understanding of the specific task from both input and output text perspectives,facilitating the classification of multiple attributes of merchandise.Experimental results on the Clothing Fit Data dataset demonstrate that the dual pre-trained T5 model outperforms both non-pre-trained models and other baseline models in attribute classification,validating the effectiveness of the proposed approach.

Key words: Dual pretraining, Multi-attribute classification, Large language model, T5, Commodity attribute classification