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TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification [19.911923049421137]
We introduce TACLR, the first retrieval-based method for Product Attribute Value Identification (PAVI) It formulates PAVI as an information retrieval task by encoding product profiles and candidate values into embeddings and retrieving values based on their similarity to the item embedding. It offers three key advantages: (1) it effectively handles implicit and OOD values while producing normalized outputs; (2) it scales to thousands of categories, tens of thousands of attributes, and millions of values; and (3) it supports efficient inference for high-load industrial scenarios.
arXiv Detail & Related papers (2025-01-07T14:45:30Z)
Self-Refinement Strategies for LLM-based Product Attribute Value Extraction [51.45146101802871]
This paper investigates applying two self-refinement techniques to the product attribute value extraction task. The experiments show that both self-refinement techniques fail to significantly improve the extraction performance while substantially increasing processing costs. For scenarios with development data, fine-tuning yields the highest performance, while the ramp-up costs of fine-tuning are balanced out as the amount of product descriptions increases.
arXiv Detail & Related papers (2025-01-02T12:55:27Z)
Exploring Large Language Models for Product Attribute Value Identification [25.890927969633196]
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.
arXiv Detail & Related papers (2024-09-19T12:09:33Z)
Using LLMs for the Extraction and Normalization of Product Attribute Values [47.098255866050835]
This paper explores the potential of using large language models (LLMs) to extract and normalize attribute values from product titles and descriptions. We introduce the Web Data Commons - Product Attribute Value Extraction (WDC-PAVE) benchmark dataset for our experiments.
arXiv Detail & Related papers (2024-03-04T15:39:59Z)
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction [59.94977231327573]
We propose a multi-task learning model with value generation/classification and attribute prediction called JPAVE. Two variants of our model are designed for open-world and closed-world scenarios. Experimental results on a public dataset demonstrate the superiority of our model compared with strong baselines.
arXiv Detail & Related papers (2023-11-07T18:36:16Z)
Product Information Extraction using ChatGPT [69.12244027050454]
This paper explores the potential of ChatGPT for extracting attribute/value pairs from product descriptions. Our results show that ChatGPT achieves a performance similar to a pre-trained language model but requires much smaller amounts of training data and computation for fine-tuning.
arXiv Detail & Related papers (2023-06-23T09:30:01Z)
A Unified Generative Approach to Product Attribute-Value Identification [6.752749933406399]
We explore a generative approach to the product attribute-value identification (PAVI) task. We finetune a pre-trained generative model, T5, to decode a set of attribute-value pairs as a target sequence from the given product text. Experimental results confirm that our generation-based approach outperforms the existing extraction and classification-based methods.
arXiv Detail & Related papers (2023-06-09T00:33:30Z)
What Makes Good In-Context Examples for GPT-$3$? [101.99751777056314]
GPT-$3$ has attracted lots of attention due to its superior performance across a wide range of NLP tasks. Despite its success, we found that the empirical results of GPT-$3$ depend heavily on the choice of in-context examples. In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples.
arXiv Detail & Related papers (2021-01-17T23:38:40Z)

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