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    <abstract>Recent progress in developing general purpose text embedders has been driven by training on ever-growing corpora of synthetic LLM-generated data. Nonetheless, no publicly available synthetic dataset exists, posing a barrier to studying its role for generalization. To address this issue, we first reproduce and publicly release the synthetic data proposed by Wang et al. (2024) (Mistral-E5). Our synthetic data is high quality and leads to consistent improvements in performance. Next, we critically examine where exactly synthetic data improves model generalization. Our analysis reveals that benefits from synthetic data are sparse and highly localized to individual datasets. Moreover, we observe trade-offs between the performance on different categories and data that benefits one task, degrades performance on another. Our findings highlight the limitations of current synthetic data approaches for building general-purpose embedders and challenge the notion that training on synthetic data leads to more robust embedding models across tasks.</abstract>
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%0 Conference Proceedings
%T Understanding the Influence of Synthetic Data for Text Embedders
%A Springer, Jacob Mitchell
%A Adlakha, Vaibhav
%A Reddy, Siva
%A Raghunathan, Aditi
%A Mosbach, Marius
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F springer-etal-2025-understanding
%X Recent progress in developing general purpose text embedders has been driven by training on ever-growing corpora of synthetic LLM-generated data. Nonetheless, no publicly available synthetic dataset exists, posing a barrier to studying its role for generalization. To address this issue, we first reproduce and publicly release the synthetic data proposed by Wang et al. (2024) (Mistral-E5). Our synthetic data is high quality and leads to consistent improvements in performance. Next, we critically examine where exactly synthetic data improves model generalization. Our analysis reveals that benefits from synthetic data are sparse and highly localized to individual datasets. Moreover, we observe trade-offs between the performance on different categories and data that benefits one task, degrades performance on another. Our findings highlight the limitations of current synthetic data approaches for building general-purpose embedders and challenge the notion that training on synthetic data leads to more robust embedding models across tasks.
%R 10.18653/v1/2025.findings-acl.1160
%U https://aclanthology.org/2025.findings-acl.1160/
%U https://doi.org/10.18653/v1/2025.findings-acl.1160
%P 22551-22567

Markdown (Informal)

[Understanding the Influence of Synthetic Data for Text Embedders](https://aclanthology.org/2025.findings-acl.1160/) (Springer et al., Findings 2025)

• Understanding the Influence of Synthetic Data for Text Embedders (Springer et al., Findings 2025)

ACL

• Jacob Mitchell Springer, Vaibhav Adlakha, Siva Reddy, Aditi Raghunathan, and Marius Mosbach. 2025. Understanding the Influence of Synthetic Data for Text Embedders. In Findings of the Association for Computational Linguistics: ACL 2025, pages 22551–22567, Vienna, Austria. Association for Computational Linguistics.