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To test whether an AI model works reliably in Indian languages, evaluate it on the languages, scripts, tasks, and real use conditions it will face—not just on a single multilingual score or translated English questions. Pair task-matched benchmarks with native-language examples, report results separately by language and task, probe for unequal treatment with matched scenarios, and have fluent human reviewers check both answers and automated graders.
Start by defining what “works” means for your deployment
A model can translate well yet misunderstand local context, answer a factual question inaccurately, or produce fluent text that is incomplete or unsuitable for the task. Testing should begin with the product’s intended use, not with whichever benchmark is easiest to run.
Write down who will use the system, what they will ask it to do, and what happens when it gets an answer wrong. A low-stakes drafting assistant and a system that influences access to a service do not have the same failure costs or testing needs. Define the relevant channels too: typed input, voice input, or both. If users will speak to the system, testing text alone will not establish how the complete voice experience performs.
Build a language-by-task matrix before selecting datasets. For each supported language, list the tasks and conditions that matter:
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- Language and script: Test the scripts users are expected to use. Include alternate script or transliteration patterns only where they are relevant to actual use.
- Register and dialect: Include the formal, conversational, regional, or domain-specific forms users are likely to produce. “Language supported” is not a substitute for specifying which forms have been tested.
- Code-mixing: If people may combine languages in one message, include those messages. IndQA, for example, includes Hinglish because code-switching is common in conversation.
- Task: Separate capabilities such as summarization, question answering, translation, instruction following, and culturally grounded reasoning. Success on one does not prove success on another.
- Domain and consequence: Identify the subject matter and the impact of an incorrect, incomplete, or unfair response.
Do not create a test set by translating every English prompt and treating the translations as equivalent. Translation is useful for controlled comparisons, but a translated item can miss natural phrasing, local references, or cultural assumptions. Native-language prompts and culturally grounded cases add coverage that a translation-only test cannot provide.
Choose benchmarks that match the question you need to answer
Benchmarks measure defined items and scoring methods, not every capability of a model. Select complementary resources when the deployment spans multiple tasks, and read each benchmark’s scope before interpreting its results.
| Resource | Useful for | Published scope and limits |
|---|---|---|
| IndQA | Cultural knowledge and reasoning in Indian-language contexts | OpenAI’s 2025 description reports 2,278 questions across 12 languages and 10 cultural domains, developed with 261 domain experts. Questions are written in the target languages and include English translations for auditability, ideal answers, and weighted rubric criteria scored by a model grader. It is not a general test of every language capability. |
| MILU | Multi-task language understanding and India-specific knowledge | The AI4Bharat repository describes 11 Indic languages, eight domains, and 41 subjects. Its paper summary reports variation by language resource level and weaker results in culturally specific domains than in general STEM. A benchmark score does not guarantee performance in a particular deployment. |
| IndicGenBench | User-facing generation, including cross-lingual summarization, translation, and question answering | The 2024 Association for Computational Linguistics paper describes 29 Indic languages, 13 scripts, and four language families, with human-curated evaluation data. Its breadth helps with generation coverage; use task-appropriate scoring and human review. |
| Indic-QA | Context-grounded extractive and abstractive question answering | The 2025 Association for Computational Linguistics paper covers 11 major Indian languages. In that study, Translate-Test (translate input to English, process it, then translate back) outperformed multilingual models particularly in low-resource settings. That finding is specific to the study and is not a general recommendation to deploy Translate-Test. |
| Indic-Bias | Bias and stereotype probes | A Government of India Principal Scientific Adviser working-paper summary describes 85 Indian identity groups, plausibility, judgment, and generation tasks, and evaluation of 14 popular LLMs. Check the benchmark’s primary documentation and coverage for your use case; the summary does not establish that every relevant group or harm is included. |
| PARIKSHA | Understanding multilingual evaluation and human/model grader agreement | The EMNLP 2024 study evaluated 30 models in 10 Indic languages using 90,000 human and 30,000 LLM-based evaluations. It found that human/LLM agreement varied by evaluation mode and dropped in direct assessment, especially for Bengali and Odia; it also reported evidence of self-bias in a GPT-based evaluator. This is evidence about evaluation reliability, not a universal model ranking. |
These counts describe the scope of datasets and studies. They are not model accuracy scores, and they do not show that a benchmark covers all dialects, users, or deployment risks. When comparing evaluation routes, consider language and script coverage, task fit, whether prompts were authored natively or translated, cultural and domain relevance, scoring transparency, human validation, subgroup coverage, contamination risk, and whether results can be reported by language rather than only pooled.
Build a test set that reflects real use
Use public benchmarks as a foundation, then add deployment-specific items. A benchmark may have strong coverage for one capability but omit the product’s domain, input format, or high-impact failure cases. Keep the distinction clear in your report: identify which results come from a named benchmark and which come from your own evaluation set.
Include both controlled and natural prompts
Controlled prompts help isolate a difference—for example, whether a response changes when only a name or social detail changes. Natural prompts show how people actually phrase requests, including informal language or code-mixing. Use both where appropriate. If every test is carefully standardized, the evaluation may miss ordinary user variation; if every item is uncontrolled, it may be difficult to tell what caused a score difference.
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Keep language slices separate
Score each target language individually. Within a language, break out meaningful dimensions such as task, script, register, or subgroup when the sample supports a useful comparison. A pooled figure can conceal a weak result in one language, especially when the evaluation contains unequal numbers of examples per language. If you publish a combined result, explain how it is calculated and how the language weights were chosen.
Document the item and scoring design
For each evaluation set, record what a correct or acceptable answer looks like. Exact-match scoring can be suitable when a question has a well-defined answer, but it may mark a semantically correct alternative as wrong. For open-ended responses, define task-specific criteria before grading rather than relying on a vague overall impression. Preserve the prompts, expected answers or rubrics, and any rules used to interpret variants so that another evaluator can understand the score.
Use metrics that fit the task
There is no single accuracy measure that makes translation, open-ended assistance, and cultural reasoning directly comparable. Choose measures based on the behavior you are testing, and state what each measure can and cannot establish.
- Exact match or accuracy: Use when the output has a clearly defined target, such as a fixed answer or a multiple-choice selection. Inspect errors as well as the percentage; a small number of high-consequence mistakes may matter more than a larger number of harmless variations.
- Rubric-based review: For generated responses, score relevant dimensions such as correctness, completeness, relevance, fluency, instruction following, and groundedness. Not every task needs every dimension, and a fluent answer should not receive credit for factual correctness unless it is correct.
- Task-specific checks: For context-grounded question answering, check whether the response is supported by the provided context. For generation, examine whether it completes the requested task and preserves important meaning. For translation, judge fidelity and appropriateness for the intended use rather than treating grammatical fluency as enough.
- Slice-level reporting: Present scores by language and task, then by other meaningful test conditions where there is enough evidence. Include sample sizes and describe the scoring procedure so readers can see how much each result represents.
IndQA illustrates one structured approach: its questions have weighted, expert-authored rubric criteria and are scored by a model grader. That is a benchmark design choice, not proof that model grading is inherently reliable. The metrics above are practical choices based on differences among task designs, not a universal standard prescribed by the benchmark authors.
Test bias through paired behavior, not sentiment alone
A sentiment score cannot establish whether a model treats people fairly. Bias testing should ask whether the model changes its judgments or outputs in consequential ways when identity or social context changes while the task stays comparable.
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Create matched scenarios that differ in one relevant attribute at a time. Depending on the product, examine whether the model changes a plausibility judgment, recommendation, access-related decision, explanation, or description. Keep the underlying facts and request as similar as possible; otherwise, a response difference may reflect a changed scenario rather than the identity detail you intended to test.
- Define the potential harm. Specify the decision or representation that could disadvantage a person or group, and explain why it matters in this deployment.
- Choose identity and context dimensions. Include the groups and intersections relevant to the intended users and the decision. Do not assume that a benchmark’s group list automatically matches your population.
- Write matched prompts. Change one identity or social-context detail while keeping the rest of the prompt stable. Include realistic native-language wording for each language being tested rather than relying exclusively on translated scenarios.
- Score the difference. Use a defined rubric for the behavior at issue—for example, whether the recommendation is equally plausible, whether the model applies the same evidentiary standard, or whether generated descriptions rely on harmful stereotypes.
- Review examples with people. Ask fluent reviewers with relevant cultural and domain knowledge to examine concerning outputs and ambiguous cases. Record the reasoning behind a finding rather than labeling a model unfair based on one isolated answer.
- Check coverage and limits. Report which groups, languages, tasks, and conditions were tested, and name important omissions. A benchmark score is evidence about its tested cases, not proof that all relevant harms have been ruled out.
Indic-Bias is one relevant resource: the Government of India working-paper summary describes plausibility, judgment, and generation tasks across 85 identity groups, with 14 LLMs evaluated. Treat those figures as a description of the summarized benchmark scope, not a guarantee of complete representation or a fairness certification.
Use human reviewers to check both answers and graders
Human review is important when judging cultural nuance, naturalness, or harm, but “human-rated” is not automatically a reliable result. Reviewers need the language fluency and cultural or subject-matter knowledge relevant to the task, plus a clear rubric and a process for handling disagreement.
Before scoring a large set, give reviewers a small set of examples and align on how the rubric should be applied. Ask them to record reasons for uncertain or disputed judgments. Examine disagreement by language and evaluation dimension; disagreement can reveal an unclear rubric, differing interpretations, or a genuinely ambiguous answer. Do not resolve every disagreement by taking a simple average if the underlying judgments are not comparable.
Automated graders can help scale review, but validate them against human judgments on the same items. PARIKSHA studied 30 models across 10 Indic languages with 90,000 human and 30,000 LLM-based evaluations. Its finding that agreement changed with evaluation mode, especially for Bengali and Odia in direct assessment, is a warning against assuming a grader behaves consistently across languages. The reported evidence of self-bias in a GPT-based evaluator is another reason to treat an AI judge as a measurement instrument that needs calibration and scrutiny, not as ground truth.
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Compare machine and human judgments by language, task, and rubric dimension. Inspect cases where they disagree, and check whether the grader systematically favors a particular style or its own kind of answer. If a grader is used to evaluate a model from the same family, treat that setup as a possible source of bias and report it. Keep human review available for high-impact or culturally ambiguous cases rather than assuming automated scoring removes the need for expertise.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the evaluation reproducible and interpret failures carefully
Evaluation results can become difficult to interpret if the tested model, prompt, dataset, or scoring procedure changes without a record. Preserve enough detail for another person to understand what was measured and, where possible, repeat it.
- Record the model name and version, date of evaluation, and any system or task instructions supplied to it.
- Save the exact prompts, response settings, dataset version, and scoring code or rubric.
- Identify who reviewed outputs, their relevant language or domain qualifications, and how disagreements were handled.
- Keep a held-out set where feasible, and assess whether benchmark items may have appeared in model training data. Contamination can be difficult to rule out completely; a held-out set reduces one risk but is not proof that exposure never occurred.
- Preserve recurring error examples alongside aggregate scores, while handling personal or sensitive information appropriately.
PARIKSHA identifies benchmark contamination and cultural mismatch in translated tests as evaluation challenges. These concerns are reasons to document test design and interpret results cautiously; they do not mean that every benchmark is contaminated or that translation-based tests are useless.
When a language slice performs poorly, first determine whether the problem is concentrated in a task, script, domain, or prompt type. Review examples to distinguish among possible issues such as misunderstanding, unsupported claims, incomplete answers, or failures to follow instructions. Then use a targeted follow-up test to see whether the pattern recurs. Avoid presenting one “Indian language accuracy” number unless its aggregation method, language weights, sample, and scoring rules are explicit.
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A benchmark result is one input to a release decision, not a substitute for deciding what level of failure is acceptable. Establish the decision criteria before seeing the results where possible. The required evidence should reflect the intended use and the consequence of error.
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For each language-task combination, make a clear decision: proceed, limit the feature, add human review, or delay deployment until a known failure is addressed. If the system will be available in a language but tested only for one task, state that boundary; do not imply broader capability. If reviewers find a harmful pattern, document the affected conditions and determine whether the product can avoid the risky use while the issue is addressed.
Repeat evaluation when the model, prompt, data, or product behavior changes in a way that could affect results. Keep versions of the test set and score reports so that a change in performance can be connected to a change in the system or evaluation method. After release, user-reported failures can identify cases missing from the test set, but they do not replace structured testing before deployment.
What a useful report should include
A reader should be able to tell what was tested, how it was scored, where it worked, and what remains uncertain. Include:
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- The intended users, languages, scripts, tasks, channels, and domains in scope.
- The benchmarks and any deployment-specific test items, with their versions or dates where available.
- Per-language and per-task results, sample sizes, scoring methods, and any aggregation formula.
- Bias-test dimensions and group coverage, plus examples of recurring failures that can be shared safely.
- Human reviewer qualifications, grader validation results, and how disagreements were handled.
- Known limitations, including untested dialects, scripts, domains, or consequences, and any unresolved contamination concerns.
State plainly that results apply to the tested items, model version, and conditions. That makes a report more useful than a broad claim that a model “supports Indian languages” or is “unbiased,” neither of which follows from a single benchmark score.
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