Most LLMs determine their token inventories by using byte-pair encoding, which algorithmically induces sub-word tokens from a body of text. So even in English y

Most LLMs determine their token inventories by using byte-pair encoding, which algorithmically induces sub-word tokens from a body of text. So even in English you might see a word like "proselytization" tokenized apart into "_pro", "selyt", "iz", "ation", and non-English languages will probably (depending on their proportional representation in the training corpus) also receive token allocations in the BPE vocabulary. Here's actual output from the GPT-4o tokenizer for English and Hindi: >>> [enc.decode([x]) for x in enc.encode("proselytization")] ['pros', 'ely', 't', 'ization'] >>> [enc.decode([x]) for x in enc.encode("पर्यावरणवाद")] ['पर', '्य', 'ावरण', 'वाद']