Customer data is often stored as records in Customer Relations Management
systems (CRMs). Data which is manually entered into such systems by one of more
users over time leads to data replication, partial duplication or fuzzy
duplication. This in turn means that there no longer a single source of truth
for customers, contacts, accounts, etc. Downstream business processes become
increasing complex and contrived without a unique mapping between a record in a
CRM and the target customer. Current methods to detect and de-duplicate records
use traditional Natural Language Processing techniques known as Entity
Matching. In this paper we show how using the latest advancements in Large
Language Models and Generative AI can vastly improve the identification and
repair of duplicated records. On common benchmark datasets we find an
improvement in the accuracy of data de-duplication rates from 30 percent using
NLP techniques to almost 60 percent using our proposed method.

使用最新的大型语言模型和生成式人工智能技术，在常见基准数据集上使用我们提出的方法，能够将数据去重的准确率从使用自然语言处理技术的 30% 提高到近 60%。

使用 GenAI 进行重复检测

Duplicate Detection with GenAI

Customer data typically is held in database systems, which can be seen as
rule-based knowledge base, whereas businesses increasingly want to benefit from
the capabilities of large, pre-trained language models.
In this technical report, we describe a case study of how a commercial rule
engine and an integrated neural chatbot may be integrated, and what level of
control that particular integration mode leads to. We also discuss alternative
ways (including past ways realized in other systems) how researchers strive to
maintain control and avoid what has recently been called model "hallucination".

客户数据通常保存在数据库系统中，这些系统可以看作是基于规则的知识库，而企业越来越希望从大型预训练语言模型的能力中受益。本技术报告中，我们描述了一个商业规则引擎和一个集成的神经聊天机器人的案例研究，以及这种特定集成模式所带来的控制级别。我们还讨论了研究人员为保持控制和避免最近被称为模型 “幻觉” 的方式的替代方法（包括在其他系统中实现的过去方法）。

混合聊天机器人中的控制

Control in Hybrid Chatbots

Customer-centric marketing campaigns generate a large portion of e-commerce
website traffic for Walmart. As the scale of customer data grows larger,
expanding the marketing audience to reach more customers is becoming more
critical for e-commerce companies to drive business growth and bring more value
to customers. In this paper, we present a scalable and efficient system to
expand targeted audience of marketing campaigns, which can handle hundreds of
millions of customers. We use a deep learning based embedding model to
represent customers and an approximate nearest neighbor search method to
quickly find lookalike customers of interest. The model can deal with various
business interests by constructing interpretable and meaningful customer
similarity metrics. We conduct extensive experiments to demonstrate the great
performance of our system and customer embedding model.

本篇论文介绍了一种可扩展、高效的系统，利用基于深度学习的嵌入模型和近似最近邻搜索方法，将目标的营销受众扩大，并能构建可以解释和有意义的客户相似度度量标准，实验结果表明该系统和客户嵌入模型效果显著。