用电商表格生成精准商品评价，提升大模型在电商场景的实用性。
eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables
- 基于商品表格数据，让大模型生成属性相关的个性化评论。
- 在标准指标上显著提升生成内容的准确性和流畅度。
- 适合电商智能客服、自动评测等需要结构化生成的应用场景。
大型语言模型（LLMs）在多个领域表现出卓越能力，但在电商领域的应用仍受限于缺乏特定领域数据集。为此，我们提出了eC-Tab2Text，一个专为电商设计的新数据集，涵盖详细商品属性和用户查询。基于该数据集，我们聚焦于从产品表格生成文本，使大模型能够从结构化数据中生成高质量、属性特定的商品评价。通过标准Table2Text指标以及正确性、忠实性和流畅性评估，对微调模型进行了严格测试。结果表明，生成的评论在上下文准确性上实现显著提升，凸显了定制化数据集与微调方法在优化电商工作流中的变革潜力。本研究展示了大模型在电商流程中的应用前景，强调了领域专用数据集在应对行业挑战中的关键作用。
原文摘要 · Abstract (English)
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.
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