2026-06-12原文2 min readUpdated: 2026-06-12

Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation

This paper frames the transformation of abstract Persian proverbs into morally faithful stories as a constrained semantic decompression task. It introduces the Proverb Aligned Narrative Dataset (PAND) and a hybrid evaluation framework. Findings reveal a decompression gap: LLMs achieve fluency but fail to instantiate underlying moral structures. Explicit reasoning and iterative refinement partially mitigate this.

SourcearXiv Computational LinguisticsAuthor: Zahra Habibzadeh, Paria Khoshtab, Amir Mesbah, Yadollah Yaghoobzadeh

[2606.12599] Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation

[Submitted on 10 Jun 2026]

Title:Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation

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Abstract: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 \emph{decompression gap}: current LLMs often achieve strong surface-level fluency while failing to faithfully instantiate the underlying moral and causal structure encoded in proverbs. We further show that explicit reasoning and iterative refinement can partially mitigate these failures, suggesting that many decompression errors arise from difficulties in translating abstract meaning into narrative form rather than a complete lack of relevant knowledge. Our proposed task naturally extends to other forms of compressed cultural knowledge.

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2606.12599 [cs.CL]

(or arXiv:2606.12599v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.12599

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Amirhossein Mesbah [view email] [v1] Wed, 10 Jun 2026 18:54:07 UTC (3,394 KB)

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