Generative Models for Product Attribute Extraction (2023)

Abstract: Product attribute extraction is an emerging field in information extraction and e-commerce, with applications including knowledge base construction, product recommendation, and enhancing customer experiences. In this work, we explore the use of generative models for product attribute extraction. We analyze their utility with hard and soft prompting methods, and demonstrate their ability to generate implicit attribute values, which state-of-the-art sequence tagging models are unable to extract. We perform a wide range of experiments on Amazon and MAVE product attribute datasets, and are the first to present results on multilingual attribute extraction. Our results show that generative models can outperform state- of-the-art tagging models for explicit product attribute extraction while having greater data efficiency, that they have the unique ability to perform implicit attribute extraction, and that in certain settings large language models can perform competitively with finetuned models with as little as two in-context examples.

## related work the task of product attribute extraction has become increasingly relevant with the rise of ecommerce @xcite @xcite . performing closed-vocabulary attribute extraction, where product attribute values are selected from a fixed set, can be realized as a classification problem @xcite . however, such methods are less scalable as the number of products increases, and newly introduced products can contain never-before-seen attribute values. to address this, @xcite pioneered the task of open-vocabulary attribute extraction, where attribute values are not restricted to a fixed set. @xcite achieves this by using a sequence tagging architecture to mark tokens that are values for a given attribute. since @xcite , state-of-the-art models have followed extractive paradigms such as sequence tagging or extractive question answering. @xcite scales up the opentag model of @xcite by training a single model to handle all attributes, instead of training one model per attribute. @xcite improves upon the architecture of @xcite and frames the attribute extraction problem as extractive question answering. @xcite returns to sequence tagging, but uses hypernetworks and a mixture of experts to personalize model parameters for each attribute without needing to train a separate network for each. finally, @xcite introduces the mave dataset and the maveqa model, which uses the etc @xcite ) longdocument model to handle large product profiles in a sequence-tagging paradigm. three prior approaches have applied generative models to product attribute extraction. @xcite formulates the extraction problem as textinfilling and generation tasks, generating attribute values from the product title. @xcite generates attributes present in the text and their corresponding values, instead of using the attribute as as query. @xcite performs multimodal attribute extraction with image and textual inputs. our work differs from those prior in that we distinguish between implicit and explicit attribute extraction and consider generative models' ability to produce implicit values, we evaluate generative models on multilingual data, we provide results for large language models, and we explore the importance of different prompt setups. ## experiments in this section we present our experiments on product attribute extraction with generative models. we start by comparing generative models to sequencetagging architectures on implicit and explicit attribute extraction. next, we demonstrate that generative frameworks continue to perform well on multilingual attribute extraction. finally, we scale up the number of attributes with explicit attribute extraction on the mave dataset. all models are evaluated using precision and recall via set-wise comparison. precision is the likelihood that a predicted attribute value extraction is in the set of ground truth values, and recall is the likelihood that a ground-truth attribute value is predicted by the model. models may tag no values or output unknown to indicate no values are present. additional details can be found in section a.1. for all experiments, we use the 7b parameter versions of the llama @xcite and conversation-tuned vicuna @xcite large language models. we provide these models with a task description and two in-context examples, followed by the product profile to extract from. this saturates the models' context windows of 2048 tokens. training details for the finetuned models can be found in section a.3. ## conclusion in this work, we demonstrated the benefits of generative models for product attribute extraction. we showed that generative models can outperform state-of-the-art sequence tagging models on explicit attribute extraction and, unlike sequence tagging models, can perform implicit attribute extraction. next, we demonstrated that generative models perform better in low-resource settings than tagging models on both multilingual data and the mave dataset. finally, we showed that large language models can perform well with as few as two incontext examples, emphasizing generative models' data efficiency. for future work, we plan to scale up our soft prompt architecture to large language models, and push the limits of their performance with long context lengths to provide additional incontext examples. nasser zalmout and xian li. 2022. prototyperepresentations for training data filtering in weaklysupervised information extraction. in proceedings of the 2022 conference on empirical methods in natural language processing: industry track, pages 467-474, abu dhabi, uae. association for computational linguistics. nasser zalmout, chenwei zhang, xian li, yan liang, and xin luna dong. 2021. all you need to know to build a product knowledge graph. kdd '21, page 4090-4091, new york, ny, usa. association for computing machinery. guineng zheng, subhabrata mukherjee, xin luna dong, and feifei li. 2018. opentag: open attribute value extraction from product profiles. in proceedings of the acm sigkdd international conference on knowledge discovery and data mining, pages 1049-1058. association for computing machinery. lianmin zheng, wei-lin chiang, ying sheng, siyuan zhuang, zhanghao wu, yonghao zhuang, zi lin, zhuohan li, dacheng li, eric xing, et al. 2023. judging llm-as-a-judge with mt-bench and chatbot arena. arxiv preprint arxiv:2306.05685. chunting zhou, graham neubig, jiatao gu, mona diab, francisco guzmán, luke zettlemoyer, and marjan ghazvininejad. 2021. detecting hallucinated content in conditional neural sequence generation. in findings of the association for computational linguistics: acl-ijcnlp 2021, pages 1393-1404, online. association for computational linguistics.