Indi-RomCoM: Code-Mixed Benchmark for Evaluating LLMs on Romanized Indic-English Instructions
The Indi-RomCoM benchmark covers seven instruction-following tasks, four Indic languages, and three code-mixing intensity levels to systematically evaluate LLMs on Romanized Code-Mixed instructions. Results show LLMs consistently underperform, with performance degrading as code-mixing density increases; reasoning tasks degrade less than detection tasks.
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[Submitted on 29 Jun 2026]
Title:Indi-RomCoM: Code-Mixed Benchmark for Evaluating LLMs on Romanized Indic-English Instructions
View a PDF of the paper titled Indi-RomCoM: Code-Mixed Benchmark for Evaluating LLMs on Romanized Indic-English Instructions, by Avisha Das and 3 other authors
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Abstract:Romanized Code Mixing (RCM), where bilingual speakers fluidly blend local languages with English in Roman script, has emerged as the dominant form of communication across multilingual communities. While Large Language Models (LLMs) perform strongly on monolingual and native-script benchmarks, their ability to follow instructions and reason over RCM-based content remains largely unexplored. To this end, we introduce the Indi-RomCoM benchmark for facilitating systematic evaluation on Indic Romanized Code-Mixed instructions. Our benchmark spans seven instruction-following tasks, four widely spoken Indic languages, and three controlled code-mixing intensity levels. We extensively evaluate a suite of LLMs covering proprietary, open-weight, and Indic-focused models under zero- and few-shot settings. LLMs consistently underperform on RCM instructions, with performance degrading as code-mixing density increases. Furthermore, reasoning tasks suffer less degradation than detection tasks (e.g., Toxicity) because the generated explanations offer necessary context. We believe Indi-RomCoM helps the community in developing inclusive multilingual systems.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.30790 [cs.CL]
(or arXiv:2606.30790v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.30790
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Avisha Das [view email] [v1] Mon, 29 Jun 2026 18:19:24 UTC (6,494 KB)
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