@inproceedings{xia-etal-2026-hsgraphagent,
title = "{HSG}raph{A}gent: Knowledge-Graph-Guided Large Language Models for Harmonized System Code Classification",
author = "Xia, Qiang and
Zhang, Zijian and
Wang, Ao and
Wang, Wenhan and
Wang, Xiangyu and
Li, Jian",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2072/",
pages = "44761--44773",
ISBN = "979-8-89176-390-6",
abstract = "Harmonized System (HS) code classification is a hierarchically structured and regulation-constrained task, often complicated by short and noisy product descriptions. Misclassification can lead to tariff misapplication, regulatory violations, or delayed customs clearance, which in turn requires predictions to be both semantically appropriate and hierarchically valid. While large language models (LLMs) show strong semantic understanding, their unconstrained generation is poorly aligned with these requirements, often producing non-existent or hierarchically inconsistent codes. We propose HSGraphAgent a knowledge-graph-guided LLM framework that formulates HS classification as a stepwise, regulation-aware reasoning process over an explicit HS knowledge graph. By encoding hierarchical containment relations and regulatory exclusion rules, and enforcing them through a Select-Redirect mechanism, HSGraphAgent constrains inference to legally valid paths while producing explicit and traceable reasoning trajectories. Experiments on taxonomy-wide 4-digit and fine-grained 6-digit HS benchmarks demonstrate consistent improvements over direct generation and retrieval-augmented baselines, with particularly strong gains in fine-grained and regulation-sensitive classification settings."
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%0 Conference Proceedings
%T HSGraphAgent: Knowledge-Graph-Guided Large Language Models for Harmonized System Code Classification
%A Xia, Qiang
%A Zhang, Zijian
%A Wang, Ao
%A Wang, Wenhan
%A Wang, Xiangyu
%A Li, Jian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F xia-etal-2026-hsgraphagent
%X Harmonized System (HS) code classification is a hierarchically structured and regulation-constrained task, often complicated by short and noisy product descriptions. Misclassification can lead to tariff misapplication, regulatory violations, or delayed customs clearance, which in turn requires predictions to be both semantically appropriate and hierarchically valid. While large language models (LLMs) show strong semantic understanding, their unconstrained generation is poorly aligned with these requirements, often producing non-existent or hierarchically inconsistent codes. We propose HSGraphAgent a knowledge-graph-guided LLM framework that formulates HS classification as a stepwise, regulation-aware reasoning process over an explicit HS knowledge graph. By encoding hierarchical containment relations and regulatory exclusion rules, and enforcing them through a Select-Redirect mechanism, HSGraphAgent constrains inference to legally valid paths while producing explicit and traceable reasoning trajectories. Experiments on taxonomy-wide 4-digit and fine-grained 6-digit HS benchmarks demonstrate consistent improvements over direct generation and retrieval-augmented baselines, with particularly strong gains in fine-grained and regulation-sensitive classification settings.
%U https://aclanthology.org/2026.acl-long.2072/
%P 44761-44773
Markdown (Informal)
[HSGraphAgent: Knowledge-Graph-Guided Large Language Models for Harmonized System Code Classification](https://aclanthology.org/2026.acl-long.2072/) (Xia et al., ACL 2026)
ACL