Summary
People use digital assistants that try to sound like them, but current methods focus only on recent preferences, missing the deeper personality behind those choices. The authors show that personality traits can leak into writing style, and they create a new model called Atomic User Model (AUM) that breaks down a person’s identity into stable, understandable parts like psychology and behavior. Instead of just adding preferences as a prompt, their system retrieves small, relevant pieces from this layered model to guide the assistant’s responses. Tests with simulated users found this made the assistant’s style much closer to the user’s real voice, especially helping those whose style the basic assistant couldn’t match well.
What this means in practice
- •For chatbot developers: Improve chatbots by integrating a layered user personality model to generate responses that better reflect the user's stable identity.
- •For customer support teams: Enhance automated support agents to adapt their communication style closer to individual customers’ personalities for improved engagement.
Authors
B. Sankar, Deepthika S, Pawni Yadav, Amogh A S
Abstract
Assistants built on large language models are expected to write as their user would, and the dominant approach is single-channel: preferences summarised from conversation history and reinserted into context. This inverts the order of inference. Preferences are the task-dependent surface of a comparatively stable personality structure, so a system storing only preferences relearns the person whenever the task changes. First, we characterise personality seepage, where a prompt's linguistic surface carries a personality fingerprint the assistant mirrors without access to the personality behind it. Second, we propose the Atomic User Model (AUM), a human-readable representation organising a person as a stable identity nucleus with four interpretable shells (psychological, cognitive and experiential, behavioural, and social), plus cross-shell entries recording internal conflict and authenticity. Third, we treat AUM as a retrieval index over a person rather than a prompt prefix, with a pipeline where a task classifier, component-selection function and budgeted retriever return a small payload of fields at generation time. Fourth, we evaluate it with sixteen language-model-simulated participants, six style-sensitive tasks and three seeds, plus a synthetic scaling study of the retriever. Retrieving eight fields matched the style fidelity of the full user model on 23% of the context (211 tokens against 915), improved on flat preference notes by 0.24 points on a five-point scale (p < 0.001, dz = 0.50), and raised forced-choice identification of the participant's own voice from 14.9% to 42.7% (25% chance). Four pre-registered controls returned null, locating the effect in the representation rather than the search over it. The benefit is largest for participants the un-personalised assistant reproduces worst (rho = -0.61, p = 0.013): personalisation is worth most to those the default serves least.