ATLAS：通过协调关税代码分类对全球贸易中的大语言模型进行基准测试与适配
ATLAS: Benchmarking and Adapting LLMs for Global Trade via Harmonized Tariff Code Classification
AI总结：
本文提出首个基于美国海关CROSS系统的HTS代码分类基准，并推出微调模型Atlas，在10位和6位代码分类准确率上显著超越GPT-5-Thinking和Gemini-2.5-Pro-Thinking，同时成本更低且可自托管以保障数据隐私。
摘要：
在《协调关税表》（HTS）下对产品进行准确分类是全球贸易中的一个关键瓶颈，但机器学习界对此关注甚少。错误分类可能导致货物完全停运，主要邮政运营商已因海关文件不完整而暂停向美国投递。我们引入了首个HTS代码分类基准，该基准源自美国海关裁定在线检索系统（CROSS）。通过对领先的大语言模型进行评估，我们发现微调后的Atlas模型（LLaMA-3.3-70B）实现了40%的10位代码完全正确分类率和57.5%的6位代码正确分类率，分别比GPT-5-Thinking高出15个百分点，比Gemini-2.5-Pro-Thinking高出27.5个百分点。除准确率外，Atlas的成本约为GPT-5-Thinking的五分之一，约为Gemini-2.5-Pro-Thinking的八分之一，并且可以自行托管，以在高风险的贸易与合规工作流中保证数据隐私。尽管Atlas建立了一个强有力的基线，该基准仍然极具挑战性，10位代码准确率仅为40%。通过发布数据集和模型，我们旨在将HTS分类定位为一项新的社区基准任务，并邀请未来在检索、推理和对齐方面开展研究。
英文摘要：
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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