Taxon: Hierarchical Tax Code Prediction with Semantically Aligned LLM Expert Guidance

2026-01-13

AI Summary

This work proposes Taxon, a framework designed to address the compliance risks arising from inaccurate matching between e-commerce products and multi-level national tax classification systems. Taxon integrates a multimodal feature-gating mixture-of-experts (MoE) architecture, a semantic consistency verification mechanism guided by distillation from large language models, and a multi-source training pipeline that unifies tax code databases, invoice logs, and merchant registration data. To enhance structural consistency, the framework further incorporates full hierarchical path reconstruction. Evaluated on both a newly curated TaxCode dataset and public benchmarks, Taxon achieves state-of-the-art performance and has been deployed in Alibaba’s tax system, handling over 500,000 queries daily while significantly improving accuracy, interpretability, and robustness.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Tax code prediction is a crucial yet underexplored task in automating invoicing and compliance management for large-scale e-commerce platforms. Each product must be accurately mapped to a node within a multi-level taxonomic hierarchy defined by national standards, where errors lead to financial inconsistencies and regulatory risks. This paper presents Taxon, a semantically aligned and expert-guided framework for hierarchical tax code prediction. Taxon integrates (i) a feature-gating mixture-of-experts architecture that adaptively routes multi-modal features across taxonomy levels, and (ii) a semantic consistency model distilled from large language models acting as domain experts to verify alignment between product titles and official tax definitions. To address noisy supervision in real business records, we design a multi-source training pipeline that combines curated tax databases, invoice validation logs, and merchant registration data to provide both structural and semantic supervision. Extensive experiments on the proprietary TaxCode dataset and public benchmarks demonstrate that Taxon achieves state-of-the-art performance, outperforming strong baselines. Further, an additional full hierarchical paths reconstruction procedure significantly improves structural consistency, yielding the highest overall F1 scores. Taxon has been deployed in production within Alibaba's tax service system, handling an average of over 500,000 tax code queries per day and reaching peak volumes above five million requests during business event with improved accuracy, interpretability, and robustness.

Problem

• tax code prediction
• hierarchical taxonomy
• e-commerce compliance
• multi-level classification
• regulatory risk

Innovation

• hierarchical tax code prediction
• mixture-of-experts
• semantic alignment
• LLM distillation
• multi-source supervision