Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval Article Swipe
Nandan Thakur
,
Jianmo Ni
,
Gustavo Hernández Ábrego
,
John Wieting
,
Jimmy Lin
,
Daniel Cer
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.05800
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.05800
There has been limited success for dense retrieval models in multilingual retrieval, due to uneven and scarce training data available across multiple languages. Synthetic training data generation is promising (e.g., InPars or Promptagator), but has been investigated only for English. Therefore, to study model capabilities across both cross-lingual and monolingual retrieval tasks, we develop SWIM-IR, a synthetic retrieval training dataset containing 33 (high to very-low resource) languages for fine-tuning multilingual dense retrievers without requiring any human supervision. To construct SWIM-IR, we propose SAP (summarize-then-ask prompting), where the large language model (LLM) generates a textual summary prior to the query generation step. SAP assists the LLM in generating informative queries in the target language. Using SWIM-IR, we explore synthetic fine-tuning of multilingual dense retrieval models and evaluate them robustly on three retrieval benchmarks: XOR-Retrieve (cross-lingual), MIRACL (monolingual) and XTREME-UP (cross-lingual). Our models, called SWIM-X, are competitive with human-supervised dense retrieval models, e.g., mContriever-X, finding that SWIM-IR can cheaply substitute for expensive human-labeled retrieval training data. SWIM-IR dataset and SWIM-X models are available at https://github.com/google-research-datasets/SWIM-IR.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.05800
- https://arxiv.org/pdf/2311.05800
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4388650786Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2311.05800Digital Object Identifier
- Title
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Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense RetrievalWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-10Full publication date if available
- Authors
-
Nandan Thakur, Jianmo Ni, Gustavo Hernández Ábrego, John Wieting, Jimmy Lin, Daniel CerList of authors in order
- Landing page
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https://arxiv.org/abs/2311.05800Publisher landing page
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https://arxiv.org/pdf/2311.05800Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2311.05800Direct OA link when available
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Computer science, Natural language processing, Construct (python library), Artificial intelligence, Parallel corpora, Training set, Training (meteorology), Information retrieval, Labeled data, Machine translation, Programming language, Physics, MeteorologyTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.informative | 107 |
| abstract_inverted_index.monolingual | 49 |
| abstract_inverted_index.prompting), | 84 |
| abstract_inverted_index.XOR-Retrieve | 132 |
| abstract_inverted_index.capabilities | 44 |
| abstract_inverted_index.investigated | 36 |
| abstract_inverted_index.multilingual | 10, 69, 120 |
| abstract_inverted_index.supervision. | 76 |
| abstract_inverted_index.(monolingual) | 135 |
| abstract_inverted_index.cross-lingual | 47 |
| abstract_inverted_index.human-labeled | 160 |
| abstract_inverted_index.Promptagator), | 32 |
| abstract_inverted_index.mContriever-X, | 151 |
| abstract_inverted_index.(cross-lingual), | 133 |
| abstract_inverted_index.(cross-lingual). | 138 |
| abstract_inverted_index.human-supervised | 146 |
| abstract_inverted_index.(summarize-then-ask | 83 |
| abstract_inverted_index.https://github.com/google-research-datasets/SWIM-IR. | 172 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |