Automatic Product Classification in International Trade: Machine Learning and Large Language Models

Ignacio Marra de Artiñano, Franco Riottini Depetris and Christian Volpe Martincus

No 12962, IDB Publications (Working Papers) from Inter-American Development Bank

Abstract: Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform well within the dataset in which they were trained, their precision drops dramatically when implemented outside of it. In contrast, large language models (LLMs) such as GPT 3.5 show a consistently good performance across all datasets, with accuracy rates ranging between 60% and 90% depending on HS aggregation levels. Our analysis highlights the valuable role that artificial intelligence (AI) can play in facilitating product classification at scale and, more generally, in enhancing the categorization of unstructured data.

Keywords: Product Classification; machine learning; Large Language Models; Trade (search for similar items in EconPapers)
JEL-codes: C55 C81 C88 F10 (search for similar items in EconPapers)
Date: 2023-07
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-int
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Persistent link: https://EconPapers.repec.org/RePEc:idb:brikps:12962

DOI: 10.18235/0005012

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