In e-commerce, accurately extracting product attribute values from multimodal
data is crucial for improving user experience and operational efficiency of
retailers. However, previous approaches to multimodal attribute value
extraction often struggle with implicit attribute values embedded in images or
text, rely heavily on extensive labeled data, and can easily confuse similar
attribute values. To address these issues, we introduce EIVEN, a data- and
parameter-efficient generative framework that pioneers the use of multimodal
LLM for implicit attribute value extraction. EIVEN leverages the rich inherent
knowledge of a pre-trained LLM and vision encoder to reduce reliance on labeled
data. We also introduce a novel Learning-by-Comparison technique to reduce
model confusion by enforcing attribute value comparison and difference
identification. Additionally, we construct initial open-source datasets for
multimodal implicit attribute value extraction. Our extensive experiments
reveal that EIVEN significantly outperforms existing methods in extracting
implicit attribute values while requiring less labeled data.

在电子商务中，准确从多模态数据中提取产品属性值对于提高用户体验和零售商的运营效率至关重要。为了解决这些问题，我们引入了 EIVEN，一个数据和参数高效的生成框架，首次使用多模态 LLM 来进行隐含属性值提取，并通过对比学习技术降低模型混淆，构建了多模态隐含属性值提取的初始开源数据集。经过广泛实验，我们发现 EIVEN 在提取隐含属性值方面明显优于现有方法，并且需要更少的标记数据。

EIVEN：多模态 LLM 实现高效的隐式属性值提取

EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal  LLM

Product offers on e-commerce websites often consist of a textual product
title and a textual product description. In order to provide features such as
faceted product filtering or content-based product recommendation, the websites
need to extract attribute-value pairs from the unstructured product
descriptions. This paper explores the potential of using large language models
(LLMs), such as OpenAI's GPT-3.5 and GPT-4, to extract and normalize attribute
values from product titles and product descriptions. For our experiments, we
introduce the WDC Product Attribute-Value Extraction (WDC PAVE) dataset. WDC
PAVE consists of product offers from 87 websites that provide schema.org
annotations. The offers belong to five different categories, each featuring a
specific set of attributes. The dataset provides manually verified
attribute-value pairs in two forms: (i) directly extracted values and (ii)
normalized attribute values. The normalization of the attribute values requires
systems to perform the following types of operations: name expansion,
generalization, unit of measurement normalization, and string wrangling. Our
experiments demonstrate that GPT-4 outperforms PLM-based extraction methods by
10%, achieving an F1-Score of 91%. For the extraction and normalization of
product attribute values, GPT-4 achieves a similar performance to the
extraction scenario, while being particularly strong at string wrangling and
name expansion.

本文探讨使用大型语言模型（如 OpenAI 的 GPT-3.5 和 GPT-4）从产品标题和产品描述中提取和规范化属性值的潜力，并介绍了 WDC PAVE 数据集，通过实验展示 GPT-4 在产品属性值的提取和规范化上相对于 PLM 的方法获得了 91% 的 F1 得分，并且在字符串处理和名称扩展方面表现出色。

使用大语言模型进行产品属性值的提取与规范化

Using LLMs for the Extraction and Normalization of Product Attribute  Values

Automatic extraction of product attribute values is an important enabling
technology in e-Commerce platforms. This task is usually modeled using sequence
labeling architectures, with several extensions to handle multi-attribute
extraction. One line of previous work constructs attribute-specific models,
through separate decoders or entirely separate models. However, this approach
constrains knowledge sharing across different attributes. Other contributions
use a single multi-attribute model, with different techniques to embed
attribute information. But sharing the entire network parameters across all
attributes can limit the model's capacity to capture attribute-specific
characteristics. In this paper we present AdaTag, which uses adaptive decoding
to handle extraction. We parameterize the decoder with pretrained attribute
embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This
allows for separate, but semantically correlated, decoders to be generated on
the fly for different attributes. This approach facilitates knowledge sharing,
while maintaining the specificity of each attribute. Our experiments on a
real-world e-Commerce dataset show marked improvements over previous methods.

本研究提出 AdaTag，通过自适应解码使用预训练属性嵌入来生成针对不同属性的分离式解码器，从而促进了知识共享，并保持了每个属性的特异性。在实际的电子商务数据集上，与以前的方法相比，我们的实验显示出显着的改进。