Exploring Large Language Models for Product Attribute Value Identification

• URL: http://arxiv.org/abs/2409.12695v1
• Date: Thu, 19 Sep 2024 12:09:33 GMT
• Title: Exploring Large Language Models for Product Attribute Value Identification
• Authors: Kassem Sabeh, Mouna Kacimi, Johann Gamper, Robert Litschko, Barbara Plank,
• Abstract summary: Product attribute value identification (PAVI) involves automatically identifying attributes and their values from product information. Existing methods rely on fine-tuning pre-trained language models, such as BART and T5. This paper explores large language models (LLMs), such as LLaMA and Mistral, as data-efficient and robust alternatives for PAVI.
• Score: 25.890927969633196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Product attribute value identification (PAVI) involves automatically identifying attributes and their values from product information, enabling features like product search, recommendation, and comparison. Existing methods primarily rely on fine-tuning pre-trained language models, such as BART and T5, which require extensive task-specific training data and struggle to generalize to new attributes. This paper explores large language models (LLMs), such as LLaMA and Mistral, as data-efficient and robust alternatives for PAVI. We propose various strategies: comparing one-step and two-step prompt-based approaches in zero-shot settings and utilizing parametric and non-parametric knowledge through in-context learning examples. We also introduce a dense demonstration retriever based on a pre-trained T5 model and perform instruction fine-tuning to explicitly train LLMs on task-specific instructions. Extensive experiments on two product benchmarks show that our two-step approach significantly improves performance in zero-shot settings, and instruction fine-tuning further boosts performance when using training data, demonstrating the practical benefits of using LLMs for PAVI.

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