How to evaluate LLMs in Hindi and Indic languages for SaaS
Build a language-specific evaluation set for Hindi and other Indic languages, including code-mixing, scripts, safety, retrieval and native-speaker review; do not infer quality from English scores.
In this guide
Why does an Indic-language AI feature need its own evaluation?
A model can perform well on English tests and still misunderstand a Hindi request, mishandle an honorific, miss a regional phrase or produce unsafe advice in another language. IndicGenBench was designed to study generation across 29 Indic languages, scripts and task types; it is evidence that language coverage deserves measurement, not a current ranking of commercial providers. Evaluate the exact task, model, prompt, retrieval path and audience you plan to release.
Define the user task and the language variants
List what people will actually do: ask for account help, summarize a policy, search a knowledge base or request a translation. Include Hindi in Devanagari, common code-mixed Hindi-English, regional vocabulary, spelling variation and transliteration only when users enter it that way. Record the language direction for each case, such as Hindi question to Hindi answer or Hindi question to English source material.
Sample real variation without copying private conversations
Build representative prompts from approved research, synthetic cases reviewed by people and privacy-screened examples. Do not put raw customer chats into a benchmark by default. Remove or mask personal details, restrict access to evaluation data and document what it may be used for. Cover differences in literacy, dialect, age, disability and familiarity with technical language without treating one speaker as representative of every Hindi user.
Set quality criteria before comparing models
For each task, define what counts as correct, useful, understandable and safe. A translation may need to preserve key meaning; a support answer may need to cite the right policy and say when evidence is missing. Score factual support, task completion, language naturalness, unsafe content, refusal quality and whether the answer is accessible to the intended user. Keep human review for judgments that need cultural or linguistic nuance.
| Task and language direction | Input variation | Expected evidence | Quality and safety rubric | Reviewer and adjudication |
|---|---|---|---|---|
How should a team build and score an Indic-language test set?
Keep language slices visible in every report
Show results separately by language, script, task, dialect or input style when the sample supports it. An overall average can conceal that English answers are strong while Hindi answers omit evidence or that code-mixed questions fail. Report sample size and uncertainty, and avoid treating a tiny subgroup score as a stable conclusion.
Use native reviewers with a shared rubric
Ask fluent reviewers to assess meaning, terminology, tone and harmful implications. Give them clear criteria and examples; double-review consequential or ambiguous cases and record how disagreements are resolved. Machine-translated test data can help expand coverage, but its labels and phrasing may carry translation artifacts. Validate benchmark construction before treating a score as ground truth.
Evaluate changes as a product release
Run the same fixed cases when prompts, model versions, safety filters, retrieval indexes or post-processing change. Add newly discovered failures with privacy review, keep train and evaluation sets separate, and compare both regressions and improvements. A model swap that improves aggregate quality can still worsen a safety-critical language slice, so define release thresholds for each high-impact segment.
How do you make language quality safe in production?
Use language-aware refusal and escalation paths
Test whether safety controls recognize harmful requests and respond clearly in supported languages and mixed-language input. When a user needs a person, provide that route in the same language where possible. Do not assume an English moderation score or translated refusal covers all idioms, coded terms or local context; measure misses and overblocking separately.
Tell users where language support is limited
Describe supported languages and known constraints in plain language. Let people switch language or ask for a human when available, and provide a way to flag mistranslation or a culturally inappropriate response. Do not claim equal fluency unless evaluations support that claim for the tasks users will see.
Monitor failures without collecting more than needed
Track aggregate quality, language selection, correction rates and escalation outcomes with data minimization. Avoid logging full prompts or sensitive answers solely to estimate language quality. Set retention and access limits, and use sampled review only when the product's privacy commitments allow it.
Indic-language LLM evaluation: FAQs
Can an English benchmark tell me whether a model works well in Hindi?
No. It can measure English performance, but it does not establish Hindi quality, safety or usefulness. Evaluate the actual Hindi tasks and variants your product supports.
Does an Indic benchmark rank today's LLM providers?
Not by itself. Benchmarks cover particular datasets, model versions and tasks at a point in time. Use published work to inform test design, then evaluate current candidates on your own representative and permissioned cases.
Should I test transliterated Hindi?
If users enter Hindi in Latin script or mix scripts in your product, include those patterns. Keep results distinct from Devanagari so the report shows which input form succeeds or fails.
Can machine translation create all test examples?
It can help draft cases, but reviewers should check meaning and labels. Translation artifacts can make a benchmark easier or harder in ways that do not reflect real user language.
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Sources and publication record
Draft prepared 27 September 2026; engineering, security and editorial review pending · Sources checked .
- IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic LanguagesAssociation for Computational Linguistics
- Evaluation best practicesOpenAI API documentation
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1)National Institute of Standards and Technology
- Your data and model usage policies by endpointOpenAI Platform Documentation
- Production best practicesOpenAI API documentation
- ModerationOpenAI API documentation