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    <titleInfo>
        <title>Do Large Language Models Understand Double Mismatches? Evidence from Farsi</title>
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    <name type="personal">
        <namePart type="given">Maryam</namePart>
        <namePart type="family">Mohammadi</namePart>
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            <title>The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family</title>
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        <name type="personal">
            <namePart type="given">Rayyan</namePart>
            <namePart type="family">Merchant</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Karine</namePart>
            <namePart type="family">Megerdoomian</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>Large language models (LLMs) are increasingly used for communication in many languages, therefore, understanding their limitations with respect to culture-specific pragmatics is important. While LLMs perform well on statistically frequent structures, their shortcomings are most evident in rare pragmatic phenomena. This study investigates whether LLMs can generate a (rare) complex honorific mismatch in Farsi. The pattern arises at two levels:(i) a plural pronoun disagrees with a singular referent for the sake of honorification, and (ii) the related components violate the Polite Plural Generalization due to intimacy implication. This double mismatch pattern is attested in everyday speech, though it is statistically sparse. We tested GPT-4 across multiple scenarios. The results reveal that the model successfully employs the first mismatch to indicate honorific, but fails to adopt the second mismatch that simultaneously conveys intimacy. The model thus deviates from humanlike behavior at the syntax–pragmatics interface. These findings suggest that, while machine models demonstrate partial success in generating honorifics, they rely primarily on statistical patterns and lack the deeper pragmatic understanding necessary for contextual competence.</abstract>
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        <url>https://aclanthology.org/2026.silkroadnlp-1.3/</url>
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        <date>2026-03</date>
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            <start>24</start>
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%0 Conference Proceedings
%T Do Large Language Models Understand Double Mismatches? Evidence from Farsi
%A Mohammadi, Maryam
%Y Merchant, Rayyan
%Y Megerdoomian, Karine
%S The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-371-5
%F mohammadi-2026-large
%X Large language models (LLMs) are increasingly used for communication in many languages, therefore, understanding their limitations with respect to culture-specific pragmatics is important. While LLMs perform well on statistically frequent structures, their shortcomings are most evident in rare pragmatic phenomena. This study investigates whether LLMs can generate a (rare) complex honorific mismatch in Farsi. The pattern arises at two levels:(i) a plural pronoun disagrees with a singular referent for the sake of honorification, and (ii) the related components violate the Polite Plural Generalization due to intimacy implication. This double mismatch pattern is attested in everyday speech, though it is statistically sparse. We tested GPT-4 across multiple scenarios. The results reveal that the model successfully employs the first mismatch to indicate honorific, but fails to adopt the second mismatch that simultaneously conveys intimacy. The model thus deviates from humanlike behavior at the syntax–pragmatics interface. These findings suggest that, while machine models demonstrate partial success in generating honorifics, they rely primarily on statistical patterns and lack the deeper pragmatic understanding necessary for contextual competence.
%U https://aclanthology.org/2026.silkroadnlp-1.3/
%P 24-28

Markdown (Informal)

[Do Large Language Models Understand Double Mismatches? Evidence from Farsi](https://aclanthology.org/2026.silkroadnlp-1.3/) (Mohammadi, SilkRoadNLP 2026)

• Do Large Language Models Understand Double Mismatches? Evidence from Farsi (Mohammadi, SilkRoadNLP 2026)

ACL

• Maryam Mohammadi. 2026. Do Large Language Models Understand Double Mismatches? Evidence from Farsi. In The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family, pages 24–28, Rabat, Morocco. Association for Computational Linguistics.