# LLM-Based Robust Product Classification in Commerce and Compliance

arXiv: https://arxiv.org/abs/2408.05874 · HTML: https://mlsanity.com/paper/2408.05874/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 11 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://mlsanity.com/paper/2104.08826/) (2021)
- [Measure and Improve Robustness in NLP Models: A Survey](https://mlsanity.com/paper/2112.08313/) (2021)
- [InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective](https://mlsanity.com/paper/2010.02329/) (2020)
- [Towards Improving Adversarial Training of NLP Models](https://mlsanity.com/paper/2109.00544/) (2021)
- [Classification of Goods Using Text Descriptions With Sentences Retrieval](https://mlsanity.com/paper/2111.01663/) (2021)
- [Robust Product Classification with Instance-Dependent Noise](https://mlsanity.com/paper/2209.06946/) (2022)
- [Consistent Text Categorization using Data Augmentation in e-Commerce](https://mlsanity.com/paper/2305.05402/) (2023)

## Built on by (most cited first, 5 of 5)

- [EcomEdit: An Automated E-commerce Knowledge Editing Framework for Enhanced Product and Purchase Intention Understanding](https://mlsanity.com/paper/2410.14276/) (2024)
- [AttnCache: Accelerating Self-Attention Inference for LLM Prefill via Attention Cache](https://mlsanity.com/paper/2510.25979/) (2025)
- [Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach](https://mlsanity.com/paper/2508.20013/) (2025)
- [Performance Trade-offs of Optimizing Small Language Models for E-Commerce](https://mlsanity.com/paper/2510.21970/) (2025)
- [A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions](https://mlsanity.com/paper/2605.14857/) (2026)

## Abstract (arXiv)

Product classification is a crucial task in international trade, as compliance regulations are verified and taxes and duties are applied based on product categories. Manual classification of products is time-consuming and error-prone, and the sheer volume of products imported and exported renders the manual process infeasible. Consequently, e-commerce platforms and enterprises involved in international trade have turned to automatic product classification using machine learning. However, current approaches do not consider the real-world challenges associated with product classification, such as very abbreviated and incomplete product descriptions. In addition, recent advancements in generative Large Language Models (LLMs) and their reasoning capabilities are mainly untapped in product classification and e-commerce. In this research, we explore the real-life challenges of industrial classification and we propose data perturbations that allow for realistic data simulation. Furthermore, we employ LLM-based product classification to improve the robustness of the prediction in presence of incomplete data. Our research shows that LLMs with in-context learning outperform the supervised approaches in the clean-data scenario. Additionally, we illustrate that LLMs are significantly more robust than the supervised approaches when data attacks are present.