用自动化框架更新电商知识，让大模型更懂商品和用户意图。
EcomEdit: An Automated E-commerce Knowledge Editing Framework for Enhanced Product and Purchase Intention Understanding
- 用更强的LLM做判官，自动发现知识冲突并触发更新。
- 编辑后模型对商品描述和购买意图的理解显著提升。
- 适合需要实时更新商品信息的电商平台使用。
知识编辑（KE）旨在无需昂贵微调即可修正和更新大型语言模型（LLMs）中的事实性信息，以保证其准确性与相关性。尽管已在多个领域证明有效，但在电商领域的应用仍较少。然而，电商场景天然存在需更新的需求，例如及时更新产品特性及用户流行购买意图。本文首次将知识编辑引入电商领域，提出EcomEdit——一个专为电商知识与任务设计的自动化知识编辑框架。该框架利用更强大的LLM作为评判者，实现知识冲突的自动检测，并引入概念化机制以增强待编辑知识的语义覆盖范围。大量实验表明，EcomEdit能有效提升LLMs对商品描述和购买意图的理解能力；经编辑后的模型在下游电商任务中表现更优。
原文摘要 · Abstract (English)
Knowledge Editing (KE) aims to correct and update factual information in Large Language Models (LLMs) to ensure accuracy and relevance without computationally expensive fine-tuning. Though it has been proven effective in several domains, limited work has focused on its application within the e-commerce sector. However, there are naturally occurring scenarios that make KE necessary in this domain, such as the timely updating of product features and trending purchase intentions by customers, which necessitate further exploration. In this paper, we pioneer the application of KE in the e-commerce domain by presenting ECOMEDIT, an automated e-commerce knowledge editing framework tailored for e-commerce-related knowledge and tasks. Our framework leverages more powerful LLMs as judges to enable automatic knowledge conflict detection and incorporates conceptualization to enhance the semantic coverage of the knowledge to be edited. Through extensive experiments, we demonstrate the effectiveness of ECOMEDIT in improving LLMs' understanding of product descriptions and purchase intentions. We also show that LLMs, after our editing, can achieve stronger performance on downstream e-commerce tasks.
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