Paper accepted for iiWAS 2024

ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction

In order to facilitate features such as faceted product search and product comparison, e-commerce platforms require accurately structured product data, including precise attribute/value pairs. However, vendors oftentimes provide unstructured product descriptions consisting only of an offer title and a textual description. Consequently, extracting attribute values from titles and descriptions is vital for e-commerce platforms. State-of-the-art attribute value extraction (AVE) methods based on pre-trained language models (PLMs), such as BERT, face two drawbacks (i) the methods require significant amounts of task-specific training data and (ii) the fine-tuned models have problems with generalising to unseen attribute values that were not part of the training data. This paper explores the potential of using large language models (LLMs) as a more training data-efficient and more robust alternative to existing AVE methods. We propose different prompt templates for describing the target attributes of the extraction to the LLM, covering both zero-shot and few-shot scenarios. In the zero-shot scenario, textual and JSON-based target schema representations of the attributes are compared.
In the few-shot scenario, we investigate (i) the provision of example attribute values, (ii) the selection of in-context demonstrations, (iii) shuffled ensembling to prevent position bias, and (iv) fine-tuning the LLM. We evaluate the prompt templates in combination with hosted LLMs, such as GPT-3.5 and GPT-4, and open-source LLMs which can be run locally. We compare the performance of the LLMs to the PLM-based methods SU-OpenTag, AVEQA, and MAVEQA.
The highest average F1-score of 86% was achieved by GPT-4 using an ensemble of shuffled prompts that integrated a comprehensive target schema containing attribute descriptions and example values with demonstrations. Llama-3-70B performs only 3% worse than GPT-4, making it a competitive open-source alternative. Given the
same training data, this prompt/GPT-4 combination outperforms the best PLM baseline by an average of 6% F1-score. Fine-tuning GPT-3.5 results in comparable performance to GPT-4 but harms the LLM’s ability to generalize.