@inproceedings{zhan-etal-2025-slm,
title = "{SLM}-Mod: Small Language Models Surpass {LLM}s at Content Moderation",
author = "Zhan, Xianyang and
Goyal, Agam and
Chen, Yilun and
Chandrasekharan, Eshwar and
Saha, Koustuv",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.441/",
doi = "10.18653/v1/2025.naacl-long.441",
pages = "8774--8790",
ISBN = "979-8-89176-189-6",
abstract = "Large language models (LLMs) have shown promise in many natural language understanding tasks, including content moderation. However, these models can be expensive to query in real-time and do not allow for a community-specific approach to content moderation. To address these challenges, we explore the use of open-source small language models (SLMs) for community-specific content moderation tasks. We fine-tune and evaluate SLMs (less than 15B parameters) by comparing their performance against much larger open- and closed-sourced models in both a zero-shot and few-shot setting. Using 150K comments from 15 popular Reddit communities, we find that SLMs outperform zero-shot LLMs at content moderation$-11.5${\%} higher accuracy and 25.7{\%} higher recall on average across all communities. Moreover, few-shot in-context learning leads to only a marginal increase in the performance of LLMs, still lacking compared to SLMs. We further show the promise of cross-community content moderation, which has implications for new communities and the development of cross-platform moderation techniques. Finally, we outline directions for future work on language model based content moderation."
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<abstract>Large language models (LLMs) have shown promise in many natural language understanding tasks, including content moderation. However, these models can be expensive to query in real-time and do not allow for a community-specific approach to content moderation. To address these challenges, we explore the use of open-source small language models (SLMs) for community-specific content moderation tasks. We fine-tune and evaluate SLMs (less than 15B parameters) by comparing their performance against much larger open- and closed-sourced models in both a zero-shot and few-shot setting. Using 150K comments from 15 popular Reddit communities, we find that SLMs outperform zero-shot LLMs at content moderation-11.5% higher accuracy and 25.7% higher recall on average across all communities. Moreover, few-shot in-context learning leads to only a marginal increase in the performance of LLMs, still lacking compared to SLMs. We further show the promise of cross-community content moderation, which has implications for new communities and the development of cross-platform moderation techniques. Finally, we outline directions for future work on language model based content moderation.</abstract>
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%0 Conference Proceedings
%T SLM-Mod: Small Language Models Surpass LLMs at Content Moderation
%A Zhan, Xianyang
%A Goyal, Agam
%A Chen, Yilun
%A Chandrasekharan, Eshwar
%A Saha, Koustuv
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F zhan-etal-2025-slm
%X Large language models (LLMs) have shown promise in many natural language understanding tasks, including content moderation. However, these models can be expensive to query in real-time and do not allow for a community-specific approach to content moderation. To address these challenges, we explore the use of open-source small language models (SLMs) for community-specific content moderation tasks. We fine-tune and evaluate SLMs (less than 15B parameters) by comparing their performance against much larger open- and closed-sourced models in both a zero-shot and few-shot setting. Using 150K comments from 15 popular Reddit communities, we find that SLMs outperform zero-shot LLMs at content moderation-11.5% higher accuracy and 25.7% higher recall on average across all communities. Moreover, few-shot in-context learning leads to only a marginal increase in the performance of LLMs, still lacking compared to SLMs. We further show the promise of cross-community content moderation, which has implications for new communities and the development of cross-platform moderation techniques. Finally, we outline directions for future work on language model based content moderation.
%R 10.18653/v1/2025.naacl-long.441
%U https://aclanthology.org/2025.naacl-long.441/
%U https://doi.org/10.18653/v1/2025.naacl-long.441
%P 8774-8790
Markdown (Informal)
[SLM-Mod: Small Language Models Surpass LLMs at Content Moderation](https://aclanthology.org/2025.naacl-long.441/) (Zhan et al., NAACL 2025)
ACL
- Xianyang Zhan, Agam Goyal, Yilun Chen, Eshwar Chandrasekharan, and Koustuv Saha. 2025. SLM-Mod: Small Language Models Surpass LLMs at Content Moderation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 8774–8790, Albuquerque, New Mexico. Association for Computational Linguistics.