From We to Me: Theory Informed Narrative Shift with Abductive Reasoning
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 divergence). For individualistic to collectivistic transformation, we achieve comparable improvements. We show similar performance across both directions for Llama-4, and Grok-4 and competitive performance for Deepseek-R1.
Cite
@article{arxiv.2603.03320,
title = {From We to Me: Theory Informed Narrative Shift with Abductive Reasoning},
author = {Jaikrishna Manojkumar Patil and Divyagna Bavikadi and Kaustuv Mukherji and Ashby Steward-Nolan and Peggy-Jean Allin and Tumininu Awonuga and Joshua Garland and Paulo Shakarian},
journal= {arXiv preprint arXiv:2603.03320},
year = {2026}
}