Title: BLOOM Large Language Models and the Chomsky Hierarchy. Abstract: We study the performance of BLOOM large language models of different sizes on understanding 12 different languages from the Chomsky hierarchy using few-shot prompts. We investigate whether an increase in the complexity of the languages learned by the larger models can be characterized using the Chomsky hierarchy. We first show that prompting in BLOOM models enables reasoning with a good accuracy on language tasks as diverse as stack manipulation, string reversal, odds first, and interlocked pairing, when the queries are over short strings, that is, small bitwidth bit-vectors from the language. Second, we discover that the two largest models have the highest accuracy on such tasks for prompts with a fixed length, but smaller models are able to achieve similar accuracies with longer prompts. Unlike classical automata or grammar based approaches where algorithms for more complex languages in the Chomsky hierarchy can also recognize simpler languages, we find that the performance of the BLOOM large language models cannot be explained by the complexity of the languages in the Chomsky hierarchy.