Study Reveals LLMs Struggle to Faithfully Transform Persian Proverbs into Morally Accurate Stories
Researchers introduced a new dataset and evaluation framework to test how well large language models can expand Persian proverbs into coherent, culturally grounded narratives. The study frames this as 'constrained semantic decompression' and finds that current LLMs produce fluent text but frequently fail to capture the underlying moral and causal meaning of proverbs. The findings highlight a fundamental gap in how AI models handle abstract cultural knowledge, with implications for low-resource and non-Western language applications.
A team of researchers has introduced the Proverb Aligned Narrative Dataset (PAND), pairing Persian proverbs with human-written stories and explicit meaning annotations, to study whether large language models can faithfully expand compressed cultural knowledge into full narratives. The task, termed 'constrained semantic decompression,' requires models to move from a dense, abstract proverb to an engaging story that preserves its moral and causal structure. Using a hybrid evaluation framework combining human-calibrated LLM-as-a-Judge scoring with structural metrics, the researchers found a consistent 'decompression gap': models tend to generate surface-level fluent prose while failing to instantiate the deeper moral logic encoded in the source proverb. The study also tested explicit reasoning prompts and iterative refinement strategies, finding these approaches can partially reduce decompression errors. The authors interpret this as evidence that failures stem primarily from difficulty translating abstract meaning into narrative form, rather than an outright absence of cultural knowledge in the models. The researchers note the framework is generalizable to other forms of compressed cultural knowledge beyond Persian proverbs.
What's missing
The dataset size and inter-annotator agreement for human-written stories are not described in the abstract, leaving open questions about PAND's coverage and reliability. It is also unclear whether the decompression gap persists equally across proverb types or varies with proverb complexity and cultural specificity.
What different sources said
- arXiv cs.CLCenter
Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation
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