Many in One: Grounding Individuality in a Language Model

Large language models can produce many apparent personas, but this does not establish that their differences are grounded in the individuals they represent. Names, biographies, or demographics may retrieve associations learned during pretraining, and such associations need not track measured differences between actual people. We ask: can a model learn person-specific variation from observed behaviour alone, and can we read that variation back? Because natural speech entangles content with manner, we study one isolable dimension of grounded individuality, register: how speakers formulate given content. We introduce addresses, one learned speaker embedding per person, stored as a row of the model's input-embedding matrix and fitted by paraphrase inversion. We fit addresses for $640$ speakers within one Qwen2.5-7B model, on 6.7 million training words from a new voice-verified corpus of public-figure podcast speech. We evaluate generated personas on human-calibrated register axes that separate grounding, diversity, and coverage. Addresses recover person-specific register, including in episodes and shows held out from training. A speaker's own turns ground personas best, and on held-out episodes a name, prompted or written in the address's place during fine-tuning, grounds them better than an address alone. Addresses yield more distinct styles and more collective variety of ideas than a prompted name, and a name written in their place during fine-tuning yields more of both. Because addresses are parameters, we read, steer and extend them. These results show that a single language model can represent grounded person-specific variation that we can measure and interpret.