Large Language Models (LLMs) have demonstrated exceptional versatility across diverse domains, yet their application in e-commerce remains underexplored due to a lack of domain-specific datasets. To address this gap, we introduce eC-Tab2Text, a novel dataset designed to capture the intricacies of e-commerce, including detailed product attributes and user-specific queries. Leveraging eC-Tab2Text, we focus on text generation from product tables, enabling LLMs to produce high-quality, attribute-specific product reviews from structured tabular data. Fine-tuned models were rigorously evaluated using standard Table2Text metrics, alongside correctness, faithfulness, and fluency assessments. Our results demonstrate substantial improvements in generating contextually accurate reviews, highlighting the transformative potential of tailored datasets and fine-tuning methodologies in optimizing e-commerce workflows. This work highlights the potential of LLMs in e-commerce workflows and the essential role of domain-specific datasets in tailoring them to industry-specific challenges.

本研究解决了大规模语言模型在电商领域应用不足的难题，通过引入eC-Tab2Text数据集，专注于从电商产品表格生成高质量的产品评论。研究表明，针对特定领域的数据集和微调方法能够显著提升文本生成的准确性和流畅性，展现出LMS在电商工作流程中的变革潜力。

eC-Tab2Text：基于方面的电商产品表格文本生成