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

[Submitted on 10 Feb 2026 (v1), last revised 3 Aug 2026 (this version, v2)]

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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.

Submission history

From: Jaikrishna Manojkumar Patil [view email]
[v1] Tue, 10 Feb 2026 04:10:19 UTC (11,904 KB)
[v2] Mon, 3 Aug 2026 09:29:45 UTC (11,904 KB)