  E-commerce platforms rely on structured product descriptions, in the form of
attribute/value pairs to enable features such as faceted product search and
product comparison. However, vendors on these platforms often provide
unstructured product descriptions consisting of a title and a textual
description. To process such offers, e-commerce platforms must extract
attribute/value pairs from the unstructured descriptions. State-of-the-art
attribute/value extraction 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 to
generalize to attribute values that were not part of the training data. We
explore the potential of using large language models (LLMs) as a more training
data-efficient and more robust alternative to existing attribute/value
extraction methods. We propose different prompt templates for instructing LLMs
about the target schema of the extraction, covering both zero-shot and few-shot
scenarios. In the zero-shot scenario, textual and JSON-based approaches for
representing information about the target attributes are compared. In the
scenario with training data, 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. The
prompt templates are evaluated in combination with hosted LLMs, such as GPT-3.5
and GPT-4, and open-source LLMs based on Llama2 which can be run locally. The
best average F1-score of 86% was reached by GPT-4 using an ensemble of shuffled
prompts that combine attribute names, attribute descriptions, example values,
and demonstrations. Given the same amount of training data, this prompt/model
combination outperforms the best PLM baseline by an average of 6% F1.
