Eka-Eval : A Comprehensive Evaluation Framework for Large Language
  Models in Indian Languages
Authors: name: Samridhi Raj Sinha; name: Rajvee Sheth; name: Abhishek Upperwal; name: Mayank Singh
Published: 2025-07-02 16:07:54

Abstract: The rapid advancement of Large Language Models (LLMs) has intensified the
need for evaluation frameworks that go beyond English centric benchmarks and
address the requirements of linguistically diverse regions such as India. We
present EKA-EVAL, a unified and production-ready evaluation framework that
integrates over 35 benchmarks, including 10 Indic-specific datasets, spanning
categories like reasoning, mathematics, tool use, long-context understanding,
and reading comprehension. Compared to existing Indian language evaluation
tools, EKA-EVAL offers broader benchmark coverage, with built-in support for
distributed inference, quantization, and multi-GPU usage. Our systematic
comparison positions EKA-EVAL as the first end-to-end, extensible evaluation
suite tailored for both global and Indic LLMs, significantly lowering the
barrier to multilingual benchmarking. The framework is open-source and publicly
available at https://github.com/lingo-iitgn/ eka-eval and a part of ongoing EKA
initiative (https://eka.soket.ai), which aims to scale up to over 100
benchmarks and establish a robust, multilingual evaluation ecosystem for LLMs.