@inproceedings{chu-etal-2026-faithful,
    title = "Faithful Persona Steering under Incongruity via Dual-Stream Refinement",
    author = "Chu, Yu-An  and
      Pong, Jen-Ren  and
      Yeh, Chia-Yao  and
      Chiang, Meng-Fen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.findings-acl.1205/",
    pages = "24078--24095",
    ISBN = "979-8-89176-395-1",
    abstract = "Standard LLM personalization typically frames identity as a static retrieval task, overlooking the inherent incongruity of human personas, where stable traits coexist with atypical, context-specific stances. Existing methods struggle to reconcile these dimensions: prompting succumbs to context drift over long sequences, while fine-tuning often suppresses idiosyncratic ``quirks'' in favor of generic distributional patterns. To bridge this gap, we present QuirkyMind, a framework that disentangles identity definition from its expression. First, Traits Anchoring constructs a dual-stream latent state, fusing a sentence-level summary for semantic stability with a token-level sequence for generative control. This state is stabilized via In-Context Narrative Refinement using an alternating objective: a discriminative InfoNCE loss anchors the persona in representation space to prevent drift, while a generative cross-entropy loss ensures faithful verbalization. Finally, Persona Steered Generalization transfers the refined state to downstream tasks via parameter-efficient adapters. Empirical evaluations on Persona-Steered QA and Narrative Inference demonstrate that QuirkyMind mitigates drift, consolidating persona knowledge without erasing authentic incongruities."
}