# SLM-Mod: Small Language Models Surpass LLMs at Content Moderation

arXiv: https://arxiv.org/abs/2410.13155 · HTML: https://mlsanity.com/paper/2410.13155/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation](https://mlsanity.com/paper/2204.11788/) (2022)
- [A Just and Comprehensive Strategy for Using NLP to Address Online Abuse](https://mlsanity.com/paper/1906.01738/) (2019)
- [Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production](https://mlsanity.com/paper/2312.14972/) (2023)
- [MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention](https://mlsanity.com/paper/2406.05344/) (2024)
- [Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community](https://mlsanity.com/paper/1910.09563/) (2019)
- [Measuring the Prevalence of Anti-Social Behavior in Online Communities](https://mlsanity.com/paper/2208.13094/) (2022)
- [Measuring the Monetary Value of Online Volunteer Work](https://mlsanity.com/paper/2205.14528/) (2022)

## Built on by (most cited first, 27 of 27)

- [MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance](https://mlsanity.com/paper/2505.14483/) (2025)
- [RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions](https://mlsanity.com/paper/2601.13235/) (2026)
- [Breaking Bad Tokens: Detoxification of LLMs Using Sparse Autoencoders](https://mlsanity.com/paper/2505.14536/) (2025)
- [PAIR-SAFE: A Paired-Agent Approach for Runtime Auditing and Refining AI-Mediated Mental Health Support](https://mlsanity.com/paper/2601.12754/) (2026)
- [ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios](https://mlsanity.com/paper/2510.10998/) (2025)
- [Social Simulacra in the Wild: AI Agent Communities on Moltbook](https://mlsanity.com/paper/2603.16128/) (2026)
- [MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform](https://mlsanity.com/paper/2506.00308/) (2025)
- [ArgCMV: An Argument Summarization Benchmark for the LLM-era](https://mlsanity.com/paper/2508.19580/) (2025)
- [From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communities](https://mlsanity.com/paper/2604.03920/) (2026)
- [LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback](https://mlsanity.com/paper/2605.30273/) (2026)
- [Comparing energy consumption and accuracy in text classification inference](https://mlsanity.com/paper/2508.14170/) (2025)
- [Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2510.04641/) (2025)
- [Asking For It: Question-Answering for Predicting Rule Infractions in Online Content Moderation](https://mlsanity.com/paper/2510.06350/) (2025)
- [Quantifying Feature Importance for Online Content Moderation](https://mlsanity.com/paper/2510.19882/) (2025)
- [Are Open-Weight LLMs Ready for Social Media Moderation? A Comparative Study on Bluesky](https://mlsanity.com/paper/2602.05189/) (2026)
- [GMP: A Benchmark for Content Moderation under Co-occurring Violations and Dynamic Rules](https://mlsanity.com/paper/2603.01724/) (2026)
- [Masking or Mitigating? Deconstructing the Impact of Query Rewriting on Retriever Biases in RAG](https://mlsanity.com/paper/2604.06097/) (2026)
- [CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs](https://mlsanity.com/paper/2604.10502/) (2026)
- [AI Content Moderation in Therapy Conversations](https://mlsanity.com/paper/2605.25454/) (2026)
- [Toxic HallucinAItions: Perturbing Prompts and Tracing LLM Circuits](https://mlsanity.com/paper/2605.30913/) (2026)
- [When AI Says "I have been in similar situations": Synthetic Lived Experience in Peer-Like Caregiver Support](https://mlsanity.com/paper/2606.18057/) (2026)
- [PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails](https://mlsanity.com/paper/2607.05910/) (2026)
- [Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)](https://mlsanity.com/paper/2607.12336/) (2026)
- [Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows](https://mlsanity.com/paper/2607.13078/) (2026)
- [Fence: Specialized SLM Guardrails for LLM Applications](https://mlsanity.com/paper/2607.18268/) (2026)
- [MAVEN: A Macro-Societal Value Evaluation Framework of Multimodal Content with Compact Aligned Evaluators](https://mlsanity.com/paper/2608.18096/) (2026)
- [Detoxifying Toxic Communication: A Design Science Approach to Responsible AI](https://mlsanity.com/paper/2609.00361/) (2026)

## Abstract (arXiv)

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. Code and models can be found at https://github.com/AGoyal0512/SLM-Mod.