Researchers find LLMs learn to mimic concise telegraphic style when trained on limited data

A recent study explored how large language models adapt to data constraints. Researchers trained models on a dataset simulating the concise, abbreviated style of 19th-century telegrams. The models successfully learned to generate similarly compressed, efficient text. This experiment demonstrates how training data volume and style directly shape a model's output.