FFE-Hallu:Hallucinations in Fixed Figurative Expressions:Benchmark of Idioms and Proverbs in the Persian Language
Faezeh Hosseini, Mohammadali Yousefzadeh, Yadollah Yaghoobzadeh

TL;DR
This paper introduces FFEHallu, a benchmark for evaluating how well large language models handle fixed figurative expressions in Persian, highlighting their weaknesses in recognizing authentic idioms and avoiding hallucinations.
Contribution
It presents the first comprehensive benchmark for figurative hallucination in Persian LLMs, covering generation, detection, and translation of FFEs, revealing current model limitations.
Findings
Models like GPT-4.1 perform better in detecting fabricated FFEs.
Most models struggle with cross-lingual FFE translation.
Significant gaps exist in LLMs' understanding of figurative language.
Abstract
Figurative language, particularly fixed figurative expressions (FFEs) such as idioms and proverbs, poses persistent challenges for large language models (LLMs). Unlike literal phrases, FFEs are culturally grounded, largely non-compositional, and conventionally fixed, making them especially vulnerable to figurative hallucination. We define figurative hallucination as the generation or endorsement of expressions that sound idiomatic and plausible but do not exist as authentic figurative expressions in the target language. We introduce FFEHallu, the first comprehensive benchmark for evaluating figurative hallucination in LLMs, with a focus on Persian, a linguistically rich yet underrepresented language. FFEHallu consists of 600 carefully curated instances spanning three complementary tasks: (i) FFE generation from meaning, (ii) detection of fabricated FFEs across four controlled…
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Taxonomy
TopicsLanguage, Metaphor, and Cognition · Action Observation and Synchronization · Neurobiology of Language and Bilingualism
