Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation
Transforming a dense, abstract proverb into an engaging and morally faithful narrative requires deep cultural understanding and robust semantic grounding. We frame this problem as a \emph{constrained semantic decompression} task and study proverb-conditioned story generation as a testbed for abstraction-to-realization in large language models (LLMs). Focusing on Persian, we introduce the Proverb Aligned Narrative Dataset (PAND), pairing proverbs with human-written stories and explicit meanings. By a hybrid evaluation framework that combines human-calibrated LLM-as-a-Judge with structural metrics, we analyze model behavior across multiple prompting regimes. Our findings reveal a persistent...
| Authors | Zahra Habibzadeh, Paria Khoshtab, Amir Mesbah et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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