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From We to Me: Theory Informed Narrative Shift with Abductive Reasoning
arXiv – CS AI|Jaikrishna Manojkumar Patil, Divyagna Bavikadi, Kaustuv Mukherji, Ashby Steward-Nolan, Peggy-Jean Allin, Tumininu Awonuga, Joshua Garland, Paulo Shakarian|
🤖AI Summary
Researchers developed a neurosymbolic approach using social science theory and abductive reasoning to help Large Language Models transform text narratives while preserving core messages. The method achieved 55.88% improvement over baseline performance with GPT-4o when shifting between collectivistic and individualistic narrative frameworks.
Key Takeaways
- →Current Large Language Models struggle significantly with narrative shift tasks that preserve original core messages.
- →A new neurosymbolic approach combining social science theory with abductive reasoning was developed to address this limitation.
- →The method achieved 55.88% improvement over zero-shot LLM baselines with GPT-4o for collectivistic to individualistic transformations.
- →Similar performance improvements were demonstrated across multiple LLMs including Llama-4, Grok-4, and Deepseek-R1.
- →The approach maintains superior semantic similarity with original stories while successfully shifting narrative frameworks.
Mentioned in AI
Models
GPT-4OpenAI
LlamaMeta
GrokxAI
#llm#narrative-shift#neurosymbolic#abductive-reasoning#social-science#communication#text-transformation#research#gpt-4o
Read Original →via arXiv – CS AI
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