@inproceedings{gundam-mamidi-2026-telugueval,
title = "{T}elugu{E}val: A Comprehensive Benchmark for Evaluating {LLM} Capabilities in {T}elugu",
author = "Gundam, Revanth Kumar and
Mamidi, Radhika",
editor = "Hettiarachchi, Hansi and
Ranasinghe, Tharindu and
Plum, Alistair and
Rayson, Paul and
Mitkov, Ruslan and
Gaber, Mohamed and
Premasiri, Damith and
Tan, Fiona Anting and
Uyangodage, Lasitha",
booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/manual-author-scripts/2026.loreslm-1.20/",
pages = "212--224",
ISBN = "979-8-89176-377-7",
abstract = "Large Language Models (LLMs) excel on English reasoning tasks but falter on morphologically rich, low-resource languages such as Telugu, Tamil, and Kannada. We present TeluguEval, a human-curated reasoning benchmark created by translating GSM8K (math), Winogrande (commonsense), ARC (science), CaseHOLD (law), and Hendrycks Ethics into Telugu. We evaluate eight models spanning global (Llama-3.1-8B, Llama-2-7B, Qwen-8B, Gemma-7B, Gemini-2.0) and regional (Telugu-Llama2-7B, Indic-Gemma-7B, Sarvam-m-24B) systems. While extremely strong models such as Gemini and Sarvam-m largely retain performance in Telugu, most English-centric models suffer severe accuracy drops, often exceeding 30 to 40 points, particularly on mathematical and scientific reasoning. We further observe systematic failure modes including script sensitivity, option-selection bias, repetition loops, and unintended code-switching. Our results demonstrate that surface-level Telugu fluency does not imply robust reasoning capability, underscoring the need for Telugu-specific data, tokenization, and pretraining. TeluguEval provides a standardized testbed to drive progress on reasoning in low-resource Indian languages."
}