@inproceedings{zhang-etal-2025-survey-foundation,
title = "A Survey on Foundation Language Models for Single-cell Biology",
author = "Zhang, Fan and
Chen, Hao and
Zhu, Zhihong and
Zhang, Ziheng and
Lin, Zhenxi and
Qiao, Ziyue and
Zheng, Yefeng and
Wu, Xian",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.26/",
doi = "10.18653/v1/2025.acl-long.26",
pages = "528--549",
ISBN = "979-8-89176-251-0",
abstract = "The recent advancements in language models have significantly catalyzed progress in computational biology. A growing body of research strives to construct unified foundation models for single-cell biology, with language models serving as the cornerstone. In this paper, we systematically review the developments in foundation language models designed specifically for single-cell biology. Our survey offers a thorough analysis of various incarnations of single-cell foundation language models, viewed through the lens of both pre-trained language models (PLMs) and large language models (LLMs). This includes an exploration of data tokenization strategies, pre-training/tuning paradigms, and downstream single-cell data analysis tasks. Additionally, we discuss the current challenges faced by these pioneering works and speculate on future research directions. Overall, this survey provides a comprehensive overview of the existing single-cell foundation language models, paving the way for future research endeavors."
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<abstract>The recent advancements in language models have significantly catalyzed progress in computational biology. A growing body of research strives to construct unified foundation models for single-cell biology, with language models serving as the cornerstone. In this paper, we systematically review the developments in foundation language models designed specifically for single-cell biology. Our survey offers a thorough analysis of various incarnations of single-cell foundation language models, viewed through the lens of both pre-trained language models (PLMs) and large language models (LLMs). This includes an exploration of data tokenization strategies, pre-training/tuning paradigms, and downstream single-cell data analysis tasks. Additionally, we discuss the current challenges faced by these pioneering works and speculate on future research directions. Overall, this survey provides a comprehensive overview of the existing single-cell foundation language models, paving the way for future research endeavors.</abstract>
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%0 Conference Proceedings
%T A Survey on Foundation Language Models for Single-cell Biology
%A Zhang, Fan
%A Chen, Hao
%A Zhu, Zhihong
%A Zhang, Ziheng
%A Lin, Zhenxi
%A Qiao, Ziyue
%A Zheng, Yefeng
%A Wu, Xian
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhang-etal-2025-survey-foundation
%X The recent advancements in language models have significantly catalyzed progress in computational biology. A growing body of research strives to construct unified foundation models for single-cell biology, with language models serving as the cornerstone. In this paper, we systematically review the developments in foundation language models designed specifically for single-cell biology. Our survey offers a thorough analysis of various incarnations of single-cell foundation language models, viewed through the lens of both pre-trained language models (PLMs) and large language models (LLMs). This includes an exploration of data tokenization strategies, pre-training/tuning paradigms, and downstream single-cell data analysis tasks. Additionally, we discuss the current challenges faced by these pioneering works and speculate on future research directions. Overall, this survey provides a comprehensive overview of the existing single-cell foundation language models, paving the way for future research endeavors.
%R 10.18653/v1/2025.acl-long.26
%U https://aclanthology.org/2025.acl-long.26/
%U https://doi.org/10.18653/v1/2025.acl-long.26
%P 528-549
Markdown (Informal)
[A Survey on Foundation Language Models for Single-cell Biology](https://aclanthology.org/2025.acl-long.26/) (Zhang et al., ACL 2025)
ACL
- Fan Zhang, Hao Chen, Zhihong Zhu, Ziheng Zhang, Zhenxi Lin, Ziyue Qiao, Yefeng Zheng, and Xian Wu. 2025. A Survey on Foundation Language Models for Single-cell Biology. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 528–549, Vienna, Austria. Association for Computational Linguistics.