Study on LLMs for Promptagator-Style Dense Retriever Training
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912623724855296 |
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| author | Gwon, Daniel Jedidi, Nour Lin, Jimmy |
| author_facet | Gwon, Daniel Jedidi, Nour Lin, Jimmy |
| contents | Promptagator demonstrated that Large Language Models (LLMs) with few-shot prompts can be used as task-specific query generators for fine-tuning domain-specialized dense retrieval models. However, the original Promptagator approach relied on proprietary and large-scale LLMs which users may not have access to or may be prohibited from using with sensitive data. In this work, we study the impact of open-source LLMs at accessible scales ($\leq$14B parameters) as an alternative. Our results demonstrate that open-source LLMs as small as 3B parameters can serve as effective Promptagator-style query generators. We hope our work will inform practitioners with reliable alternatives for synthetic data generation and give insights to maximize fine-tuning results for domain-specific applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Study on LLMs for Promptagator-Style Dense Retriever Training Gwon, Daniel Jedidi, Nour Lin, Jimmy Information Retrieval Computation and Language Promptagator demonstrated that Large Language Models (LLMs) with few-shot prompts can be used as task-specific query generators for fine-tuning domain-specialized dense retrieval models. However, the original Promptagator approach relied on proprietary and large-scale LLMs which users may not have access to or may be prohibited from using with sensitive data. In this work, we study the impact of open-source LLMs at accessible scales ($\leq$14B parameters) as an alternative. Our results demonstrate that open-source LLMs as small as 3B parameters can serve as effective Promptagator-style query generators. We hope our work will inform practitioners with reliable alternatives for synthetic data generation and give insights to maximize fine-tuning results for domain-specific applications. |
| title | Study on LLMs for Promptagator-Style Dense Retriever Training |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2510.02241 |