  Natural language processing (NLP) has seen significant advancements with the
advent of large language models (LLMs). However, substantial improvements are
still needed for languages other than English, especially for specific domains
like the applications of Mercosur Common Nomenclature (NCM), a Brazilian
Harmonized System (HS). To address this gap, this study uses TeenyTineLLaMA, a
foundational Portuguese LLM, as an LLM source to implement the NCM application
processing. Additionally, a simplified Retrieval-Augmented Fine-Tuning (RAFT)
technique, termed SLIM-RAFT, is proposed for task-specific fine-tuning of LLMs.
This approach retains the chain-of-thought (CoT) methodology for prompt
development in a more concise and streamlined manner, utilizing brief and
focused documents for training. The proposed model demonstrates an efficient
and cost-effective alternative for fine-tuning smaller LLMs, significantly
outperforming TeenyTineLLaMA and ChatGPT-4 in the same task. Although the
research focuses on NCM applications, the methodology can be easily adapted for
HS applications worldwide.
