@inproceedings{pal-2026-prompt,
    title = "Prompt Stylometry for On-Device Affect-Adaptive {AI}: A Feasibility Study in Linguistic Signal Detection and Response Steering",
    author = "Pal, Debmalya",
    editor = "Habernal, Ivan  and
      Ghanavati, Sepideh  and
      Haghighi, Sara  and
      Ramesh, Krithika  and
      Igamberdiev, Timour  and
      Wilson, Shomir",
    booktitle = "Proceedings of the Seventh Workshop on Privacy in Natural Language Processing",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.privatenlp-main.6/",
    pages = "80--95",
    ISBN = "979-8-89176-397-5",
    abstract = "Every user prompt contains latent linguistic signals beyond its explicit semantic content: lexical choice, hedging, sentence structure, and discourse patterns, that reflect the user{'}s affective state and cognitive style. Yet most large language models are optimized for generalized assistant behavior rather than explicit adaptation to these fine-grained signals. We introduce Prompt Stylometry, a framework for detecting affective and cognitive-style signals directly from user prompts and using them to steer response generation. We study two categories of signals: affect-related cues associated with emotional states, and cognitive-style cues associated with patterns such as analytical, exploratory, self-critical, or indecisive reasoning. This inference capability, however, creates substantial privacy risks: any system processing prompts server-side could implicitly profile users' psychological states without their knowledge or consent. This motivates our core design choice of a fully on-device architecture in which no interaction data leaves the user{'}s device. We benchmark three annotation paradigms, lexicon-based, neural, and generative, across 600 synthetic prompts spanning 30 stylometric profiles, and evaluate affect-adaptive response steering across two small language model families under 5B parameters. Our results show systematic differences in both signal detection behavior and downstream steering responsiveness across annotation methods and model families, demonstrating the feasibility of privacy-preserving affect-adaptive AI on consumer hardware while identifying annotation paradigm sensitivity and cross-profile transfer as key open challenges."
}