Evaluate search quality by testing the languages, scripts, query styles, and tasks your users actually use—not by relying on a single multilingual score. Build a test matrix, have qualified speakers judge relevance, measure both ranking and coverage, and report results separately for each important language and script. If the system generates answers, evaluate answer correctness as well as retrieval.
Define what the search system is expected to do
Choose metrics only after defining the task. A system that finds documents has different success criteria from one that searches across languages, handles spoken queries, or generates an answer from retrieved evidence.
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- Monolingual retrieval: Does a query in a language find useful documents in that language?
- Cross-lingual retrieval: Can a query in one language find relevant documents in another?
- Spoken-query retrieval: Can the system retrieve useful material from speech input, including errors introduced during transcription?
- Retrieval-augmented generation: Does the system retrieve relevant evidence, and is its final answer correct?
For document search, assess ranking and whether relevant items are found. For generated answers, add an answer-quality measure; good retrieval alone does not show that the response is correct.
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Language coverage and script coverage are different things. Record the language and writing system for both queries and documents, and include combinations that reflect actual product use. Indian-language users may type a language in its usual script, write it in Roman letters, mix scripts, or search across languages.
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| Dimension | Test cases to include | What the slice can reveal |
|---|---|---|
| Language | Each language in the intended audience, including Indo-Aryan and Dravidian languages where relevant | Whether quality differs by language rather than appearing acceptable only in aggregate |
| Script | The script used for each language in the product, recorded explicitly | Whether the system handles the tested writing system, not merely the language label |
| Query and document scripts | Native-script queries with native-script documents; Romanized queries with native-script documents; native-script queries with Romanized documents; and Romanized pairs when they occur | Whether matching works across the script combinations users encounter |
| Spelling and transliteration | Common alternate Roman spellings and spelling variants alongside canonical forms | Whether a query depends too heavily on one transliteration convention |
| Query mode and task | Typed, spoken, language-mixed, cross-lingual, or answer-seeking queries as applicable | Whether quality changes with input mode or the expected outcome |
Transliteration rarely has one universally used spelling. A Hindi term might appear in Roman script as “pahala,” “pahalaa,” “pehla,” or “pahila.” The mixed-script information-retrieval paper by Gupta and colleagues uses examples of this kind to illustrate why alternate spellings should be tested directly, rather than normalized away in the evaluation set.
There is no universal minimum language list for every product. For a sense of possible breadth, FIRE 2024’s spoken cross-lingual task covers English, Hindi, and Bengali query/document language combinations, while its document collection includes Bengali, Gujarati, Hindi, Marathi, and English. A separate FIRE 2026 MAST Indic track description covers nine Indic languages and nine scripts. These are examples of benchmark scope, not required coverage for every search product.
Build queries and relevance labels that match real use
Use representative queries and documents from the product’s domain. Relevance depends on what the user meant and what counts as a useful result in that context, so have people who can judge the relevant language and information need label the results. Keep labels tied to the query-language and document-language combination.
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For each query, record how it was created: for example, authored by a native speaker, translated, machine-translated, or transcribed from speech. These sources of test material provide different kinds of evidence and should not be treated as interchangeable.
- IndicIRSuite (2023) translates MS MARCO queries and passages into eleven Indian languages.
- IndicRAGSuite (2025 preprint) reports manually translating 1,000 MS MARCO development queries into thirteen Indian languages; its training resources include material from nineteen Indian-language Wikipedias.
- FIRE 2024’s spoken cross-lingual task reports native-speaker queries and relevance judgments. Its paper reports 50 spoken training queries and 100 spoken test queries.
These resources can support comparative experiments, but their query creation, domains, and task designs differ. For example, newspaper-based FIRE material does not by itself establish performance on a product’s other domains.
Choose metrics for the failure you need to detect
No single metric answers whether search is good. Choose measures that correspond to user impact, then report their cut-offs and aggregation clearly.
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| Metric | What it measures | Useful when |
|---|---|---|
| MRR | How high the first relevant result appears in the ranking | Users often need a useful result near the top. FIRE’s spoken-query task used MRR as its primary retrieval metric. |
| Recall@k | How many judged-relevant items appear within the first k results | Missing relevant evidence is costly, or a downstream system needs a sufficiently complete result set. FIRE reports Recall@100 and Recall@1000. |
| NDCG@k | Ranking quality within the first k results, accounting for graded relevance | Some results are more useful than others and labels distinguish degrees of relevance. IndicIRSuite reports NDCG@10 in its model comparisons. |
| Answer accuracy | Whether the final generated response answers correctly | The search system returns a synthesized answer. MAST describes Exact Match accuracy with adjudication for semantically equivalent answers. |
State the cut-off (such as @10 or @100), query count, relevance scale, and aggregation method. Say whether the result is macro-averaged by language or pooled across queries; an unexplained combined score can hide a weak language or script slice.
Compare systems on the same evaluation set
Compare a lexical baseline with at least one suitable neural or multilingual retrieval baseline. Give each system the same corpus, queries, relevance labels, and metric definitions. If testing generated answers, keep the answer task and scoring procedure consistent too.
IndicIRSuite’s published comparisons show why benchmark results need their baseline and scope attached. The authors report a 47.47% average MRR@10 improvement over their INDIC-MARCO baseline, excluding Oriya; a 12.26% average NDCG@10 improvement over the MIRACL Bengali and Hindi baselines; and a 20% MRR@100 improvement over the Mr.Tydi Bengali baseline. These are relative results on the paper’s benchmarks and comparisons, not a forecast of gains for another product.
When comparing two systems, use the same test set and examine language and script coverage, monolingual versus cross-lingual behavior, query mode, ranking, recall, and answer correctness where relevant. A change in dataset or judgments makes it harder to attribute a score difference to the system itself.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use benchmarks for the questions they can answer
Published benchmarks make useful reference points, but their scope determines what conclusions they support. Check the task, corpus, languages, query construction, and metrics before applying a benchmark result to your product.
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| Resource | What it covers | How to interpret it |
|---|---|---|
| FIRE 2024 Spoken Query Cross-Lingual IR | Spoken cross-lingual retrieval; English, Hindi, and Bengali query/document combinations; documents in Bengali, Gujarati, Hindi, Marathi, and English; MRR, Recall@100, and Recall@1000 | A reference for spoken and cross-lingual evaluation, with a small reported set of 50 training and 100 test queries. |
| MAST @ FIRE 2026 | Track documentation describes nine Indic languages and scripts, around 100,000 English BrowseComp-Plus documents, and 50 queries per language; it includes relevance-based recall, search-turn efficiency, and final answer accuracy | A reference for multilingual agentic search and answers. Track and leaderboard details are dynamic, so check the live documentation before citing a result. |
| IndicIRSuite (2023) | Translated MS MARCO resources and monolingual neural information-retrieval models in eleven Indian languages | Useful for model comparisons within its benchmark setup; the authors note domain limitations in earlier newspaper-based FIRE material. |
| IndicRAGSuite (2025 preprint) | Retrieval and response-generation resources, including manually translated development queries in thirteen Indian languages | Relevant to retrieval-augmented generation experiments; its translation and training-resource provenance should accompany interpretation. |
| MTEB Indic v1 | 25 languages and 20 tasks across seven task types, including retrieval and reranking as well as other embedding tasks | A broader embedding benchmark, not a retrieval-only measure of production search quality. Its live models and results may change. |
A benchmark score is evidence about its test conditions. It does not establish how a commercial system performs across other domains, scripts, or user populations.
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Inspect results by slice and investigate failures
Publish results by language, script, query mode, and task, with sample sizes and uncertainty where available. Make the query/document language pairing visible for cross-lingual evaluation. This lets a reader see whether an apparent improvement applies broadly or only to selected slices.
Review failed and borderline cases instead of treating the aggregate metric as an explanation. Useful categories include transliteration and spelling variants, named entities, morphology, speech-recognition errors, and cross-language document matching. Record product-specific sampling and review choices; the cited benchmark descriptions do not define one validated production audit protocol for every domain.
Quick Recap
- Define the task. Specify whether the product does monolingual or cross-lingual retrieval, spoken search, or retrieval-augmented answering.
- Choose coverage. Select languages from the intended audience and record the script of queries and documents.
- Build the test set. Include realistic query modes and spelling variants; record whether queries were authored, translated, machine-translated, or transcribed.
- Label relevance. Use judges qualified for the language and information need, and preserve query/document language pairings.
- Score consistently. Use ranking and coverage metrics for retrieval, plus answer accuracy when the product responds with generated text.
- Compare and report. Hold data and judgments fixed across baselines, publish per-slice results, and review failures before drawing product-wide conclusions.
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