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        <title>Far Out: Evaluating Language Models on Slang in Australian and Indian English</title>
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        <namePart type="given">Deniz</namePart>
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    <abstract>Language models exhibit systematic performance gaps when processing text in non-standard language varieties, yet their ability to comprehend variety-specific slang remains underexplored for several languages. We present a comprehensive evaluation of slang awareness in Indian English (en-IN) and Australian English (en-AU) across seven state-of-the-art language models. We construct two complementary datasets: WEB, containing 377 web-sourced usage examples from Urban Dictionary, and GEN, featuring 1,492 synthetically generated usages of these slang terms, across diverse scenarios. We assess language models on three tasks: target word prediction (TWP), guided target word prediction (TWP*) and target word selection (TWS). Our results reveal four key findings: (1) Higher average model performance TWS versus TWP and TWP*, with average accuracy score increasing from 0.03 to 0.49 respectively (2) Stronger average model performance on WEB versus GEN datasets, with average similarity score increasing by 0.03 and 0.05 across TWP and TWP* tasks respectively (3) en-IN tasks outperform en-AU when averaged across all models and datasets, with TWS demonstrating the largest disparity, increasing average accuracy from 0.44 to 0.54. These findings underscore fundamental asymmetries between generative and discriminative competencies for variety-specific language, particularly in the context of slang expressions despite being in a technologically rich language such as English.</abstract>
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%0 Conference Proceedings
%T Far Out: Evaluating Language Models on Slang in Australian and Indian English
%A Dilsiz, Deniz Kaya
%A Srirag, Dipankar
%A Joshi, Aditya
%S Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F dilsiz-etal-2026-far
%X Language models exhibit systematic performance gaps when processing text in non-standard language varieties, yet their ability to comprehend variety-specific slang remains underexplored for several languages. We present a comprehensive evaluation of slang awareness in Indian English (en-IN) and Australian English (en-AU) across seven state-of-the-art language models. We construct two complementary datasets: WEB, containing 377 web-sourced usage examples from Urban Dictionary, and GEN, featuring 1,492 synthetically generated usages of these slang terms, across diverse scenarios. We assess language models on three tasks: target word prediction (TWP), guided target word prediction (TWP*) and target word selection (TWS). Our results reveal four key findings: (1) Higher average model performance TWS versus TWP and TWP*, with average accuracy score increasing from 0.03 to 0.49 respectively (2) Stronger average model performance on WEB versus GEN datasets, with average similarity score increasing by 0.03 and 0.05 across TWP and TWP* tasks respectively (3) en-IN tasks outperform en-AU when averaged across all models and datasets, with TWS demonstrating the largest disparity, increasing average accuracy from 0.44 to 0.54. These findings underscore fundamental asymmetries between generative and discriminative competencies for variety-specific language, particularly in the context of slang expressions despite being in a technologically rich language such as English.
%U https://aclanthology.org/2026.vardial-1.2/
%P 18-31

Markdown (Informal)

[Far Out: Evaluating Language Models on Slang in Australian and Indian English](https://aclanthology.org/2026.vardial-1.2/) (Dilsiz et al., VarDial 2026)

• Far Out: Evaluating Language Models on Slang in Australian and Indian English (Dilsiz et al., VarDial 2026)

ACL

• Deniz Kaya Dilsiz, Dipankar Srirag, and Aditya Joshi. 2026. Far Out: Evaluating Language Models on Slang in Australian and Indian English. In Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 18–31, Rabat, Morocco. Association for Computational Linguistics.