# Bielik Guard: Efficient Polish Language Safety Classifiers for LLM Content Moderation

arXiv: https://arxiv.org/abs/2602.07954 · HTML: https://mlsanity.com/paper/2602.07954/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 8 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://mlsanity.com/paper/2312.06674/) (2023)
- [ShieldGemma: Generative AI Content Moderation Based on Gemma](https://mlsanity.com/paper/2407.21772/) (2024)
- [Qwen3Guard Technical Report](https://mlsanity.com/paper/2510.14276/) (2025)
- [Safeguarding Large Language Models: A Survey](https://mlsanity.com/paper/2406.02622/) (2024)
- [Current state of LLM Risks and AI Guardrails](https://mlsanity.com/paper/2406.12934/) (2024)
- [Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation](https://mlsanity.com/paper/2410.18565/) (2024)
- [PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods](https://mlsanity.com/paper/2402.13350/) (2024)
- [Bielik v3 Small: Technical Report](https://mlsanity.com/paper/2505.02550/) (2025)
- [Bielik 11B v3: Multilingual Large Language Model for European Languages](https://mlsanity.com/paper/2601.11579/) (2025)
- [PL-Guard: Benchmarking Language Model Safety for Polish](https://mlsanity.com/paper/2506.16322/) (2025)

## Built on by (most cited first, 2 of 2)

- [Making Bielik LLM Reason (Better): A Field Report](https://mlsanity.com/paper/2603.10640/) (2026)
- [Baszta: Data-Centric Fine-Tuning of a Polish Multi-Label Safety Classifier](https://mlsanity.com/paper/2609.29266/) (2026)

## Abstract (arXiv)

As Large Language Models (LLMs) become increasingly deployed in Polish language applications, the need for efficient and accurate content safety classifiers has become paramount. We present Bielik Guard, a family of compact Polish language safety classifiers comprising two model variants: a 0.1B parameter model based on MMLW-RoBERTa-base and a 0.5B parameter model based on PKOBP/polish-roberta-8k. Fine-tuned on a community-annotated dataset of 6,885 Polish texts, these models classify content across five safety categories: Hate/Aggression, Vulgarities, Sexual Content, Crime, and Self-Harm. Our evaluation demonstrates that both models achieve strong performance on multiple benchmarks. The 0.5B variant offers the best overall discrimination capability with F1 scores of 0.791 (micro) and 0.785 (macro) on the test set, while the 0.1B variant demonstrates exceptional efficiency. Notably, Bielik Guard 0.1B v1.1 achieves superior precision (77.65%) and very low false positive rate (0.63%) on real user prompts, outperforming HerBERT-PL-Guard (31.55% precision, 4.70% FPR) despite identical model size. The models are publicly available and designed to provide appropriate responses rather than simple content blocking, particularly for sensitive categories like self-harm.