Evaluate search quality in Indian languages by testing each language, script, query mode, and search task separately. Judge results with people who understand the language and the user’s information need, measure ranking and retrieval coverage, and add answer-accuracy checks when the system generates answers. A single average score can hide serious failures in one language or script.
Start by defining what “good search” means for your product
The right evaluation depends on what the system is expected to do. A document search engine should find relevant documents and rank them usefully. A spoken-query system must also handle speech input. A cross-lingual system may need to match a query in one language to documents in another. A retrieval-augmented generation (RAG) system must retrieve useful evidence and produce a correct answer from it.
Evaluate retrieval separately from any answer-generation stage. For a system that returns a generated answer, retrieval metrics alone cannot show whether the final response is correct. Conversely, answer accuracy alone can conceal whether the system found the right evidence consistently.
- Monolingual retrieval: query and documents are in the same language.
- Cross-lingual retrieval: the query and relevant documents may be in different languages.
- Spoken search: queries are spoken, transcribed, or otherwise processed from audio.
- RAG or answer search: the system retrieves evidence and then returns a synthesized response.
- Mixed-script search: users may enter Romanized text, native script, or a mixture, and documents may use either script.
Write down the intended user, information need, corpus, and task before choosing a score. A search system for a particular product domain should be evaluated on representative queries and documents from that domain, not only on a convenient public benchmark.
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Build a language-and-script test matrix
Language coverage is not the same as script coverage. Record both for every test slice. For example, a Hindi evaluation should state whether it tests Devanagari, Romanized Hindi, or both; a Punjabi evaluation should identify Gurmukhi or another script actually present in the product’s traffic. Include Indo-Aryan and Dravidian languages when they are relevant to the intended audience rather than treating one language as a proxy for all Indian-language search.
A useful matrix crosses four dimensions:
| Dimension | What to record | Examples of distinct slices |
|---|---|---|
| Language | Query language and document language | Hindi query to Hindi documents; Hindi query to English documents |
| Script | Script used by the query and by the document | Devanagari query to Devanagari documents; Romanized query to Devanagari documents |
| Query mode | How the query was entered or produced | Typed native script; typed Romanization; spoken query; language-mixed query |
| Task | What success means for the product | Document retrieval; cross-lingual retrieval; retrieval followed by an answer |
Test combinations that occur in real use, including native-script queries against Romanized documents and the reverse where applicable. Do not assume there is one standard transliteration. For Hindi, the mixed-script information-retrieval paper gives variants such as “pahala,” “pahalaa,” “pehla,” and “pahila.” A system that succeeds only on one spelling may fail users who enter another.
FIRE’s spoken cross-lingual task offers an example of broad language coverage, not a universal minimum: its benchmark describes Hindi, Bengali, Telugu, Tamil, Gujarati, Kannada, Punjabi, Malayalam, and Odia across their corresponding scripts. Select languages and scripts from the product’s actual audience and document collection.
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Assemble queries, documents, and relevance judgments
Use a corpus that reflects the content users search and a query set that reflects their real information needs. For each query, record its language, script, mode, and provenance. Useful provenance categories include native-authored, translated, machine-translated, and transcribed from speech. These categories matter because they represent different evidence about how people actually search.
Have people who can judge the language and intended information need assess which documents are relevant. Preserve the connection between a query and the language of the judged document: a label for a Hindi query to Hindi documents does not automatically establish relevance for a Hindi query to English documents. Document the relevance scale and adjudication procedure so readers can interpret the scores.
Benchmark datasets can support controlled comparisons, but their construction affects what conclusions they justify. IndicIRSuite translates MSMARCO queries and passages into eleven Indian languages. IndicRAGSuite reports manually translating 1,000 MS MARCO development queries into thirteen Indian languages and using training resources from nineteen Indian-language Wikipedias. Neither resource should be treated as interchangeable with native-authored product queries. State how each evaluation set was produced and validated.
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Choose metrics that expose different failure modes
No single metric answers every evaluation question. Report the measure, its cutoff where applicable, query count, relevance scale, aggregation method, and whether results are macro-averaged by language or pooled across queries.
| Metric | What it helps answer | Use it when |
|---|---|---|
| MRR | How high the first relevant result appears | A user is likely to benefit from finding one useful result quickly. |
| Recall@k | Whether relevant material appears within the first k retrieved results | Coverage of relevant evidence or documents at a specified depth matters. |
| NDCG@k | How well results are ordered when relevance can have degrees | Judgments distinguish more relevant from less relevant results and ranking position matters. |
| Answer accuracy | Whether the returned answer is correct | The system synthesizes a response from retrieved evidence; report this alongside retrieval measures. |
FIRE’s spoken-query task used MRR as its primary retrieval measure and reported Recall@100 and Recall@1000. IndicIRSuite reports NDCG@10 in its model comparisons. MAST describes relevance-label-based recall and Exact Match answer accuracy, with adjudication for semantically equivalent answers. These examples illustrate different evaluation choices; they do not establish one required metric set for every search product.
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Avoid collapsing several measures into an unexplained composite score. If you produce an overall score, show the underlying metric values and explain how the aggregation works.
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Compare systems on the same evidence
For a fair comparison, hold the corpus, query set, relevance judgments, query slices, and metric definitions fixed. Compare a lexical baseline with at least one suitable neural or multilingual retrieval baseline where those approaches fit the task. A difference in test data or relevance labels can otherwise be mistaken for a difference in system quality.
IndicIRSuite’s 2023 paper reports benchmark-specific relative improvements: 47.47% average MRR@10 improvement against its INDIC-MARCO baseline excluding Oriya; 12.26% average NDCG@10 improvement against the MIRACL Bengali and Hindi baselines; and 20% MRR@100 improvement against the Mr.Tydi Bengali baseline. These figures belong to those experiments and baselines. They are not expected gains for another system or evidence that the same model will improve production search by those amounts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Report results by language, script, and task
Show the score for each meaningful slice, along with its sample size and uncertainty where available. A pooled average can conceal a weak result for one language or script, particularly when query counts differ across slices. MAST describes per-language retrieval evaluation and aggregation by resource tier; a product evaluation should make its own language-level outcomes similarly visible.
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When reviewing low-performing slices, inspect examples rather than relying on scores alone. Look for failures involving transliteration and spelling variants, named entities, morphology, speech recognition, and cross-language document matching. The sources describe varied scripts and query tasks but do not establish a validated, universal production audit design. Specify the product-specific sampling and review choices rather than presenting them as a standard.
Use benchmarks as bounded evidence
Public benchmarks are useful for repeatable comparisons, but a result applies to the dataset, task, and evaluation setup used. FIRE’s spoken-query task is a research benchmark with 50 spoken training queries and 100 spoken test queries. Its paper describes English, Hindi, and Bengali query/document language combinations, a collection including Bengali, Gujarati, Hindi, Marathi, and English documents, and native-speaker queries in English, Gujarati, Hindi, and Bengali. Those details make it useful for understanding a particular task setup, not for claiming broad production coverage.
MAST @ FIRE 2026 documentation describes nine Indic languages and nine scripts, around 100,000 English BrowseComp-Plus documents, 50 queries per language, and evaluation involving relevance-based recall, search-turn efficiency, and final answer accuracy. Its track and leaderboard are dynamic, so check the live benchmark documentation before quoting current results.
Other resources answer different questions. IndicRAGSuite targets retrieval and response generation, while MTEB (Indic, v1) describes 25 languages and 20 tasks across seven task types, including retrieval and reranking as well as bitext mining, classification, clustering, pair classification, and semantic similarity. MTEB’s breadth does not mean every task directly measures a production search experience.
IndicIRSuite also notes a limitation of earlier FIRE material: newspaper-based data is domain-specific. A benchmark score on news should not be generalized to commerce, government services, health information, or another domain without relevant evaluation evidence.
Quick Recap
A practical evaluation checklist
- Define whether the product performs monolingual retrieval, cross-lingual retrieval, spoken search, or retrieval plus answer generation.
- List the target languages and scripts, including Romanized and mixed-script forms if they occur in real traffic.
- Use representative queries and documents, and document whether queries are native-authored, translated, machine-translated, or speech-transcribed.
- Obtain relevance judgments from people able to evaluate each language and information need; keep query/document language combinations explicit.
- Choose metrics for ranking, coverage, graded relevance, and answer correctness as appropriate; state cutoffs and aggregation methods.
- Compare systems on the same corpus, queries, labels, and metric definitions.
- Publish per-language, per-script, and per-task results, with counts and uncertainty where available, then inspect concrete failures.
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