Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case

• URL: http://arxiv.org/abs/2607.26780v1
• Date: Wed, 29 Jul 2026 11:23:10 GMT
• Title: Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case
• Abstract summary: Large language models (LLMs) can process and generate text.<n>LLMs' strong generalization capability makes them promising for domains with limited labeled data.<n>We apply generative IE to the product domain, explicitly addressing efficiency, generalizability, and data privacy constraints.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability of large language models (LLMs) to process and generate text has introduced potential for applications in information extraction (IE). While it's debated whether LLMs outperform smaller fine-tuned models for classification tasks, their strong generalization capability makes them promising for domains with limited labeled data available for fine-tuning. This advantage is particularly relevant for the emerging application of the digital product passport (DPP), where the problem space is broad but domain-specific data remains scarce. Motivated by this use case, we apply generative IE to the product domain, explicitly addressing efficiency, generalizability, and data privacy constraints. We propose a two-step validation method that integrates a PLM block into the generative IE pipeline and thereby leverages LLMs' correction capability. We discover that such a validation task enhances LLM performance, particularly on the extraction of weakly expressed, low-salience entities that appear sparsely throughout the text. For certain entities, the performance of mid-size models can even reach levels comparable to larger models, and the improvement of first-step PLM predictions also enhance the final LLM output. Nevertheless, the effects on the smallest open-source LLMs (e.g., Llama-3.2 3B) is limited. Based on the findings, we develop a demo application for product information extraction that utilizes locally deployed LLMs, targeting further adaptations to real-world DPP use cases.

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