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indic-eval is an open-source LLM evaluation framework specifically designed for the Indian language ecosystem. It covers Hindi comprehension, Hinglish (Hindi-English mixed language) sentiment analysis, translation quality assessment, and Indian cultural reasoning ability testing, filling the gap left by English-centric benchmark tests.
Section 01
indic-eval is an open-source LLM evaluation framework specifically designed for the Indian language ecosystem. It covers Hindi comprehension, Hinglish (Hindi-English mixed language) sentiment analysis, translation quality assessment, and Indian cultural reasoning ability testing, filling the gap left by English-centric benchmark tests.
Section 02
Mainstream evaluation frameworks like lm-evaluation-harness and HELM have at least three critical issues when applied to Indian languages:
First, the phenomenon of code-switching is ignored. Real Indian online texts are not purely Hindi or English; instead, there is a large amount of Hinglish (Hindi-English mixed language). For example, sentences like "Yaar ye movie bilkul bakwaas thi" require the model to understand the meaning of Romanized Hindi embedded in informal English grammar. No standard benchmark test covers this linguistic phenomenon.
Second, the lack of cultural grounding. A model may correctly translate the word "Onam" but have no idea that it refers to a traditional festival in Kerala, India. Cultural reasoning is an independent, testable ability, not just a matter of language translation.
Third, the inapplicability of evaluation metrics. The BLEU metric was designed for European languages; for morphologically rich languages like Hindi, the chrF metric is clearly more appropriate. However, most evaluation frameworks do not make this distinction.
Section 03
indic-eval is built around these practical needs and currently includes five core evaluation tasks:
Section 04
Based on the IndicQA dataset, it uses Exact Match and Token F1 as the main evaluation metrics. This task tests the model's deep understanding of Hindi text—beyond just lexical matching, it requires true semantic comprehension.
Section 05
Using the FLORES-200 dataset, it uses both BLEU and chrF metrics for evaluation. chrF is particularly important for morphologically rich Hindi because it better captures character-level matches, unlike BLEU which over-relies on word boundaries.
Section 06
This is one of the most distinctive tasks of indic-eval. Built on real code-switched social media texts, it covers the language mixing phenomena common in Twitter, WhatsApp, and product reviews. The model needs to understand the emotional tendency of colloquial expressions like "bilkul bakwaas" (completely garbage).
Section 07
It tests the model's understanding of Indian classical art, festivals, geography, history, and social contexts through multiple-choice questions. This task reveals a key issue: a model may score 90 points on translation tasks but only 40 on cultural reasoning—meaning it understands the language but not the country.
Section 08
It uses ROUGE-L and chrF to evaluate the model's ability to generate summaries of Hindi text, testing its performance in long text comprehension and information compression.
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