<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kranti-vajjala-2026-mata">
    <titleInfo>
        <title>MATA: Mindful Assessment of the Telugu Abilities of Large Language Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Chalamalasetti</namePart>
        <namePart type="family">Kranti</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sowmya</namePart>
        <namePart type="family">Vajjala</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Núria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Henk</namePart>
            <namePart type="family">van den Heuvel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Krek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Toral</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resource Association</publisher>
            <place>
                <placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we introduce MATA, a novel evaluation dataset to assess the ability of Large Language Models (LLMs) in Telugu language, comprising 729 carefully curated multiple-choice and open-ended questions that span diverse linguistic dimensions. We evaluate 11 open-weight and closed-source LLMs on our dataset and present a fine-grained analysis of their performance. Further, we empirically show how LLMs rely on superficial heuristics such as answer position and distractor patterns for multiple-choice questions. Finally, we also compare LLM-as-a-judge evaluation with human evaluation for open-ended questions assess its reliability in a low-resource language. We argue that such fine-grained evaluation is essential for understanding model limitations and can inform the development of more linguistically capable LLMs, while also serving as a foundation for future research in Telugu NLP. Our dataset is available at:https://huggingface.co/datasets/TeluguLLMResearch/MATA</abstract>
    <identifier type="citekey">kranti-vajjala-2026-mata</identifier>
    <identifier type="doi">10.63317/2qyza2xt6xac</identifier>
    <location>
        <url>https://aclanthology.org/2026.lrec-1.334/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>4239</start>
            <end>4256</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T MATA: Mindful Assessment of the Telugu Abilities of Large Language Models
%A Kranti, Chalamalasetti
%A Vajjala, Sowmya
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F kranti-vajjala-2026-mata
%X In this paper, we introduce MATA, a novel evaluation dataset to assess the ability of Large Language Models (LLMs) in Telugu language, comprising 729 carefully curated multiple-choice and open-ended questions that span diverse linguistic dimensions. We evaluate 11 open-weight and closed-source LLMs on our dataset and present a fine-grained analysis of their performance. Further, we empirically show how LLMs rely on superficial heuristics such as answer position and distractor patterns for multiple-choice questions. Finally, we also compare LLM-as-a-judge evaluation with human evaluation for open-ended questions assess its reliability in a low-resource language. We argue that such fine-grained evaluation is essential for understanding model limitations and can inform the development of more linguistically capable LLMs, while also serving as a foundation for future research in Telugu NLP. Our dataset is available at:https://huggingface.co/datasets/TeluguLLMResearch/MATA
%R 10.63317/2qyza2xt6xac
%U https://aclanthology.org/2026.lrec-1.334/
%U https://doi.org/10.63317/2qyza2xt6xac
%P 4239-4256

Markdown (Informal)

[MATA: Mindful Assessment of the Telugu Abilities of Large Language Models](https://aclanthology.org/2026.lrec-1.334/) (Kranti & Vajjala, LREC 2026)

• MATA: Mindful Assessment of the Telugu Abilities of Large Language Models (Kranti & Vajjala, LREC 2026)

ACL

• Chalamalasetti Kranti and Sowmya Vajjala. 2026. MATA: Mindful Assessment of the Telugu Abilities of Large Language Models. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 4239–4256, Palma de Mallorca, Spain. ELRA Language Resource Association.