The quality of subword tokenization is critical for Large Language Models, yet evaluating tokenizers for morphologically rich Uralic languages is hampered by the lack of clean morpheme lexicons. We introduce SampoNLP, a corpus-free toolkit for morphological lexicon creation using MDL-inspired Self-Referential Atomicity Scoring, which filters composite forms through internal structural cues - suited for low-resource settings. Using the high-purity lexicons generated by SampoNLP for Finnish, Hungarian, and Estonian, we conduct a systematic evaluation of BPE tokenizers across a range of vocabulary sizes (8k–256k). We propose a unified metric, the Integrated Performance Score (IPS), to navigate the trade-off between morpheme coverage and over-splitting. By analyzing the IPS curves, we identify the “elbow points” of diminishing returns and provide the first empirically grounded recommendations for optimal vocabulary sizes (k) in these languages. Our study not only offers practical guidance but also quantitatively demonstrates the limitations of standard BPE for highly agglutinative languages. The SampoNLP library and all generated resources are made publicly available.

Large language models show significant inequality in language representation, particularly for Uralic languages. Our analysis found that existing tokenizers allocate minimal tokens to Uralic languages, highlighting this imbalance. To address this, we developed a pipeline to create clean monolingual datasets from Wikipedia articles for four Uralic languages. We trained Byte Pair Encoding (BPE) tokenizers with a vocabulary size of 256,000 tokens, though Northern Sami had only 93,187 due to limited data. Our findings revealed most tokens are unique to each language, with 8,102 shared across all four, and 25,876 shared among Estonian, Finnish, and Hungarian. Using the Compression Ratio metric, our tokenizers outperformed popular ones like LLaMA-2 and Gemma 2, reducing Finnish’s compression ratio from 3.41 to 1.18. These results demonstrate the importance of specialized tokenizers for underrepresented languages, improving model performance and lowering costs. By sharing our tokenizers and datasets, we provide crucial resources for further research, emphasizing the need for equitable language representation.