首个全球贸易关税分类基准，用大模型提升商品归类准确率
ATLAS: Benchmarking and Adapting LLMs for Global Trade via Harmonized Tariff Code Classification
- 构建首个基于美国海关系统的HTS分类数据集与评测基准
- 自研Atlas模型达40%十位码准确率，较GPT-5快15点，成本仅为八分之一
- 支持私有部署，适合需高安全性的跨境贸易合规场景
准确的商品海关税则分类是全球贸易的关键瓶颈，但机器学习界关注极少。错误分类可能导致货物完全滞留，主要邮政运营商已因不完整清关文件暂停对美运输。本文提出首个基于美国海关裁决在线系统（CROSS）的HTS代码分类基准。评估主流大模型发现，微调后的Atlas模型（LLaMA-3.3-70B）在十位数分类上达到40%完全正确，六位数分类达57.5%，相较GPT-5-Thinking提升15个百分点，比Gemini-2.5-Pro-Thinking高出27.5个百分点。此外，Atlas成本约为GPT-5-Thinking的五分之一、Gemini-2.5-Pro-Thinking的八分之一，且可自托管，保障高敏感贸易流程中的数据隐私。尽管表现优异，该任务仍具挑战性，十位码准确率最高仅达40%。通过开源数据集与模型，我们希望将HTS分类确立为新的社区基准任务，推动检索、推理与对齐方向的研究。
原文摘要 · Abstract (English)
Accurate classification of products under the Harmonized Tariff Schedule (HTS) is a critical bottleneck in global trade, yet it has received little attention from the machine learning community. Misclassification can halt shipments entirely, with major postal operators suspending deliveries to the U.S. due to incomplete customs documentation. We introduce the first benchmark for HTS code classification, derived from the U.S. Customs Rulings Online Search System (CROSS). Evaluating leading LLMs, we find that our fine-tuned Atlas model (LLaMA-3.3-70B) achieves 40 percent fully correct 10-digit classifications and 57.5 percent correct 6-digit classifications, improvements of 15 points over GPT-5-Thinking and 27.5 points over Gemini-2.5-Pro-Thinking. Beyond accuracy, Atlas is roughly five times cheaper than GPT-5-Thinking and eight times cheaper than Gemini-2.5-Pro-Thinking, and can be self-hosted to guarantee data privacy in high-stakes trade and compliance workflows. While Atlas sets a strong baseline, the benchmark remains highly challenging, with only 40 percent 10-digit accuracy. By releasing both dataset and model, we aim to position HTS classification as a new community benchmark task and invite future work in retrieval, reasoning, and alignment.
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