indic-eval: An Open-Source LLM Evaluation Framework Designed for Indian Languages and Culture

Key Takeaways: The current LLM evaluation field has English-centric limitations, which cannot adapt to India's multilingual (e.g., Hindi, Hinglish) and cultural scenarios; the indic-eval framework is natively built around the Indian language ecosystem and cultural background to fill this gap; it covers four core dimensions—Hindi comprehension, code-switching handling, translation quality, and cultural reasoning—supports integration of multiple models, is open-source and encourages community collaboration, and is of great significance to the development of India's AI ecosystem.

Current mainstream LLM evaluations (such as GLUE, MMLU) are English-centric, implying the assumption that 'English proficiency equals model capability'; India has 22 constitutionally recognized official languages and over 600 million Hindi speakers, but existing evaluations do not consider the uniqueness of these languages and cultural contexts, resulting in models that perform well in English possibly underperforming in Indian user scenarios.

The core design philosophy of indic-eval is that 'evaluation frameworks should reflect real language usage scenarios'—instead of simply translating English test questions, it is natively built around the Indian language ecosystem and cultural background; this ensures that evaluation results can truly reflect the model's practicality in Indian contexts.

1. Hindi Comprehension Ability: Tests reading comprehension, semantic reasoning, grammar judgment, etc., for standard written language, colloquial expressions, and dialect variants;
2. Code-Switching and Hinglish Handling: Tests language boundary recognition, semantic understanding, and generation consistency in mixed texts;
3. Translation Quality Evaluation: Mutual translation between Hindi and English/other Indian languages, with a focus on accuracy and cultural adaptability;
4. Indian Cultural Reasoning: Tests understanding of festival meanings, historical backgrounds, religious philosophy, etc., to avoid cultural misunderstandings.

The framework supports multiple model integration methods: API models (OpenAI, Anthropic, etc.), HuggingFace open-source models, and custom models; structured scorecards can be quickly obtained through API or HuggingFace integration, providing overall scores and detailed performance across various dimensions.

As an open-source project, it welcomes community contributions of test cases, feedback on issues, and sharing of experiences; native speakers from all regions of India are needed to participate in dataset construction to ensure content keeps pace with language evolution (such as new vocabulary and internet slang).

Provides capability benchmarks for developers, promoting progress in multilingual model technology; offers objective tools for enterprises to select models, reducing decision-making risks; sends a global signal that AI needs to embrace language diversity; helps enterprises ensure local adaptation of products to avoid user churn.

Currently, it mainly covers Hindi, with insufficient support for other Indian languages (such as Tamil, Telugu); content diversity and representativeness need to be improved; future plans include expanding language coverage, adding cultural scenarios, dynamically updating evaluation mechanisms, and promoting international standardization.