BM25 is a strong starting point when queries and documents share a language and well-configured tokenization; learned sparse retrieval is worth testing when model-weighted terms or vocabulary expansion may improve matching. Neither approach automatically solves cross-language search. For multilingual applications, language and script coverage—and performance on your own judged queries—should decide the design.
What lexical and learned sparse search do differently
Lexical search matches query terms to terms in documents. BM25 is a common lexical ranker: it scores matches using signals such as term frequency and document length, along with collection statistics. Its behavior depends on how text is analyzed and tokenized before indexing and querying.
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Learned sparse retrieval also represents text with weighted token dimensions. The difference is that a trained model generates those weights, which can reflect contextual importance and, in some model families, add related vocabulary beyond the text’s literal terms. “Sparse” describes the representation; it does not mean that the method is multilingual or can retrieve across languages.
That distinction matters for names, product codes, rare terminology, and other exact-match queries. Lexical matching can reward a shared token directly. Model-generated weights may help where wording differs, but their behavior depends on the model and its language coverage. Test both kinds of query rather than assuming one method subsumes the other.
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How language coverage changes the choice
When the query and document use the same language
BM25 is a strong baseline when the analyzer and tokenizer suit the language and script, and the query and document use compatible terminology. A poorly configured analyzer can undermine lexical matching; appropriate language-specific analysis is part of the retrieval system, not an optional tuning detail.
Learned sparse models also need validation for the languages in the application. SPLADE-v3-Lexical is labeled English on its model card and described there as producing a 30,522-dimensional representation. That is not evidence of multilingual capability. By contrast, the BGE-M3 authors report support for more than 100 languages, and OpenSearch describes multilingual-v1 as a multilingual sparse-retrieval model. Those claims identify candidates to evaluate, not proof of equal quality across every language or script.
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When the query and document use different languages
Language mismatch can limit lexical overlap. A sparse-vector index does not remove that problem merely by using learned weights. Cross-language retrieval needs explicit support, such as query translation, document translation, or a model trained for multilingual or cross-lingual retrieval.
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Compare translation strategies as separate variables: translating queries, translating documents, and using multilingual retrieval can behave differently. Translation quality can affect retrieval results, so record the method and evaluate it alongside the retriever. In a French-to-English scientific-document experiment, BM25 with a French analyzer performed poorly without translation; results changed when translation was introduced. That finding is specific to the experiment, not a universal ranking of approaches.
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Compare the methods against your requirements
| Decision factor | Lexical retrieval such as BM25 | Learned sparse retrieval |
|---|---|---|
| Language and script | Needs suitable analysis and tokenization for the indexed languages and scripts. | Depends on the model’s actual language and script coverage; verify the chosen checkpoint. |
| Exact names, identifiers, and rare terms | Can directly reward matching terms when normalization and tokenization preserve them. | Uses model-generated token weights; test exact-match cases rather than assuming semantic weighting preserves them. |
| Context and related vocabulary | Ranks based on term matches and collection statistics. | Weights token dimensions using a trained model; some model families can expand to related vocabulary. |
| Configuration | Requires analyzer, tokenizer, and normalization choices appropriate to the corpus. | Requires a compatible model and representation for query and document tokens, as well as language coverage checks. |
| Operational considerations | Indexing and querying rely on the configured lexical analysis and ranker. | Model inference may be required to generate query weights; document indexing and query inference must use compatible model versions and representations. |
These are design differences, not a substitute for measuring relevance. BM25 remains competitive in some settings, including long-document retrieval, as noted in the BGE-M3 model card. Whether either method works well for a particular corpus depends on the documents, queries, language mix, and retrieval depth.
What published benchmarks show—and what they do not
The reported figures below illustrate how results vary by dataset and setup. They should not be read as a cross-study leaderboard: the corpora, language tasks, metrics, model versions, and translation conditions differ.
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| Source and evaluation | Reported result | Important qualification |
|---|---|---|
| OpenSearch Project, MIRACL language tasks | Multilingual-v1: average nDCG@10 0.629; BM25: 0.305. Pruned multilingual-v1 at pruning ratio 0.1: 0.626. | Vendor-reported results; year is not stated in the opened blog text. They do not establish the same gains on another corpus. |
| Chen et al., 2024, BGE-M3 paper, MIRACL development set | BGE-M3 Sparse: nDCG@10 0.539; Dense: 0.692; Multi-vec: 0.705. | These are different retrieval modes from one model family on that development set; the scores show that modes can differ materially. |
| Valentini, Kozlowski, and Larivière, 2025, Érudit CLIR dataset | Under GPT-4 query translation, BGE-M3 Sparse: nDCG@10 0.575; BM25: 0.638. | French-to-English scientific-document experiment. Its table reports variation by translation method and metric; it is not a general retriever ranking. |
| NAVER LABS Europe, SPLADE-v3-Lexical model card | MRR@10 40.0 on MS MARCO dev; average nDCG@10 49.1 on BEIR-13. | The opened model card does not state a year. These English-oriented benchmarks are not directly comparable to MIRACL or Érudit CLIR results. |
For a local decision, use nDCG@10 to assess ordering near the top of results and Recall@k at the candidate depth passed to downstream ranking or processing. The CLIRudit paper discusses why the relevant cutoff differs between reranking and non-reranking systems. Report each metric with its dataset, languages, tokenizer, translation setup, model checkpoint, and retrieval mode.
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- Build a lexical baseline. Configure analyzers and tokenization for each language and script in the corpus. Check that normalization preserves useful matches for names, product codes, and specialist terms.
- Assemble representative judged queries. Include each important language and script, content type, and query difficulty. Add rare names and exact identifiers, as well as queries where wording may differ from the document.
- Keep the corpus snapshot fixed. Compare systems against the same documents and relevance judgments so that a change in results can be attributed to retrieval choices rather than corpus changes.
- Test cross-language strategies explicitly. Compare query translation, document translation, and multilingual retrieval where relevant. Record the translation method and quality as experimental variables.
- Evaluate compatible learned sparse models. Match query inference to the model used to produce indexed token weights. Elasticsearch’s sparse-vector query documentation specifies use of the same inference model as the indexed tokens and also allows precomputed token weights.
- Measure more than one outcome. Compare top-rank quality with nDCG@10 and recall at the candidate depth used downstream. Break results down by language, script, exact-match cases, and cross-language queries.
- Record the configuration. Log analyzer and tokenizer settings, model checkpoint and version, translation setup, pruning or sparsity controls, corpus snapshot, and candidate depth so the evaluation can be reproduced.
When to test hybrid retrieval
A hybrid system that combines lexical and learned sparse results is worth evaluating when exact term matches and model-based weighting may address different failure cases. Test it against each method alone using the same judged queries and candidate depth. Published results do not establish a universal hybrid advantage, so keep the additional complexity only if it improves the outcomes that matter for the application.
Choosing a candidate is not choosing a winner
BGE-M3 is one candidate when a team wants a model family with dense, sparse, and multi-vector retrieval modes. Its authors report support for more than 100 languages and inputs up to 8,192 tokens, while also saying that generalization to varied real-world datasets needs further investigation. OpenSearch multilingual-v1 is another candidate with published MIRACL comparisons against BM25. In both cases, model claims and benchmark scores should guide what to test—not replace testing against the application’s language distribution, queries, and infrastructure.
The useful decision is therefore local: retain a properly configured BM25 baseline, evaluate multilingual sparse models where coverage fits, and measure translation or hybrid options when language mismatch or different matching needs justify them.
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