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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Survey of Small Language Models from Penn State, ...
| Date | Stars |
|---|---|
| 2026-07-31 | 259 |
| 2026-08-06 | 259 |
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# SLMs Survey
[](https://awesome.re)  
## A Comprehensive Survey of Small Language Models: Technology, On-Device Applications, Efficiency, Enhancements for LLMs, and Trustworthiness
This repo includes the papers discussed in our comprehensive survey paper on small language models.
:book: Read the full paper here: [Paper Link](https://arxiv.org/abs/2411.03350)
## News
* **2025/10/14**: Our extended survey on SLM-LLM collaboration is released on arXiv [[Link](https://www.arxiv.org/abs/2510.13890)]
* **2025/10/11**: Gave a talk at the NLP Reading Group@PSU.
* **2025/08/24**: Our survey paper has been accepted for publication in the ACM TIST.
* **2025/08/02**: Organized a SLM Tutorial [[Link](https://fairyfali.github.io/kdd2025-tutorial/)]
* **2025/05/06**: Our tutorial on SLMs has been accepted at KDD 2025 [[Website]](https://fairyfali.github.io/kdd2025-tutorial/).
* **2025/04/27**: Gave a talk at the [WWW Workshop on LLMs for E-Commerce](https://llm4ecommerce.github.io/).
* **2025/01/17**: Presented a talk at Amazon [[Slides]](https://fairyfali.github.io/files/SLMs_Survey_Slides.pdf).
* **2024/12/28**: The second version of our SLM survey is now available on arXiv.
* **2024/11/04**: Released the first version of our comprehensive SLM survey on arXiv.
## Reference
If our survey is useful for your research, please kindly cite our [survey paper](https://dl.acm.org/doi/abs/10.1145/3768165) and [tutorial paper](https://dl.acm.org/doi/abs/10.1145/3711896.3736563):
```
@article{wang2024comprehensive,
author = {Wang, Fali and Zhang, Zhiwei and Zhang, Xianren and Wu, Zongyu and Mo, TzuHao and Lu, Qiuhao and Wang, Wanjing and Li, Rui and Xu, Junjie and Tang, Xianfeng and He, Qi and Ma, Yao and Huang, Ming and Wang, Suhang},
title = {A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness},
year = {2025},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {2157-6904},
url = {https://doi.org/10.1145/3768165},
doi = {10.1145/3768165},
note = {Just Accepted},
journal = {ACM Trans. Intell. Syst. Technol.},
month = sep,
keywords = {Small Language Models, On-Device LLMs, Domain-specific Models, Trustworthiness}
}
```
```
@inproceedings{wang2025slmtutorial,
author = {Wang, Fali and Lin, Minhua and Ma, Yao and Liu, Hui and He, Qi and Tang, Xianfeng and Tang, Jiliang and Pei, Jian and Wang, Suhang},
title = {A Survey on Small Language Models in the Era of Large Language Models: Architecture, Capabilities, and Trustworthiness},
year = {2025},
isbn = {9798400714542},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3711896.3736563},
doi = {10.1145/3711896.3736563},
booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
pages = {6173–6183},
numpages = {11},
keywords = {small language models, trustworthiness, weak-to-strong},
location = {Toronto ON, Canada},
series = {KDD '25}
}
```
```
@misc{wang2025surveycollaboratingsmalllarge,
title={A Survey on Collaborating Small and Large Language Models for Performance, Cost-effectiveness, Cloud-edge Privacy, and Trustworthiness},
author={Fali Wang and Jihai Chen and Shuhua Yang and Ali Al-Lawati and Linli Tang and Hui Liu and Suhang Wang},
year={2025},
eprint={2510.13890},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2510.13890},
}
```
## Overview of SLMs
## Timeline of SLMs
## SLMs Paper List
###Excerpt of 58,040 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7e6d286a51d58e49, topic:large-language-models, topic:llm, topic:language-model