[2603.03320] From We to Me: Theory Informed Narrative Shift with Abductive Reasoning

Computer Science > Computation and Language arXiv:2603.03320 (cs) [Submitted on 10 Feb 2026] Title:From We to Me: Theory Informed Narrative Shift with Abductive Reasoning Authors:Jaikrishna Manojkumar Patil, Divyagna Bavikadi, Kaustuv Mukherji, Ashby Steward-Nolan, Peggy-Jean Allin, Tumininu Awonuga, Joshua Garland, Paulo Shakarian View a PDF of the paper titled From We to Me: Theory Informed Narrative Shift with Abductive Reasoning, by Jaikrishna Manojkumar Patil and 7 other authors View PDF HTML (experimental) Abstract:Effective communication often relies on aligning a message with an audience's narrative and worldview. Narrative shift involves transforming text to reflect a different narrative framework while preserving its original core message--a task we demonstrate is significantly challenging for current Large Language Models (LLMs). To address this, we propose a neurosymbolic approach grounded in social science theory and abductive reasoning. Our method automatically extracts rules to abduce the specific story elements needed to guide an LLM through a consistent and targeted narrative transformation. Across multiple LLMs, abduction-guided transformed stories shifted the narrative while maintaining the fidelity with the original story. For example, with GPT-4o we outperform the zero-shot LLM baseline by 55.88% for collectivistic to individualistic narrative shift while maintaining superior semantic similarity with the original stories (40.4% improvement in KL diverg...