PLLuM: A Family of Polish Large Language Models
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Abstract (arXiv)
Large Language Models (LLMs) play a central role in modern artificial intelligence, yet their development has been primarily focused on English, resulting in limited support for other languages. We present PLLuM (Polish Large Language Model), the largest open-source family of foundation models tailored specifically for the Polish language. Developed by a consortium of major Polish research institutions, PLLuM addresses the need for high-quality, transparent, and culturally relevant language models beyond the English-centric commercial landscape. We describe the development process, including the construction of a new 140-billion-token Polish text corpus for pre-training, a 77k custom instructions dataset, and a 100k preference optimization dataset. A key component is a Responsible AI framework that incorporates strict data governance and a hybrid module for output correction and safety filtering. We detail the models' architecture, training procedures, and alignment techniques for both base and instruction-tuned variants, and demonstrate their utility in a downstream task within public administration. By releasing these models publicly, PLLuM aims to foster open research and strengthen sovereign AI technologies in Poland.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Nov 2025.
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Citing papers per year: 2026: 8.
8 papers.
- 2026Long-Context Encoder Models for Polish Language Understandingcited by 1
- 2026The Reliability Gap in Benchmark Auditing: Distribution Shift and Scale as Failure Modes of Contamination Detectioncited by 1
- 2026What properties of reasoning supervision are associated with improved downstream model quality?cited by 0
- 2026Graded Entity-Familiarity Readouts in Language Models: Polish Adaptation, Cross-Language Robustness, and Refusal Steeringcited by 0
- 2026From Zero to Hero: An Open LLM Ecosystem for Armeniancited by 0
- 2026The Limits of BPE Tokenization in Polish: Segmentation-Flexional Forms, Grammatical Anchoring, and First-Person Stability in Inflectional Language Modelscited by 0
- 2026Quantization Thresholds Replicate, Failure Modes Do Not: A Three-Model Study of Agentic Tool Use in Polish from 8-bit to 2-bitcited by 0
- 2026Wieszcz-XIX: A 3.1-Billion-Word Corpus of Pre-1918 Polish and Temporally Bounded Language Models Trained From Scratchcited by 0
- 2026Wieszcz-XIX: A 3.1-Billion-Word Corpus of Pre-1918 Polish and Temporally Bounded Language Models Trained From Scratch6 Oct 2026
- 2026Quantization Thresholds Replicate, Failure Modes Do Not: A Three-Model Study of Agentic Tool Use in Polish from 8-bit to 2-bit25 Sep 2026
- 2026From Zero to Hero: An Open LLM Ecosystem for Armenian3 Sep 2026
- 2026The Limits of BPE Tokenization in Polish: Segmentation-Flexional Forms, Grammatical Anchoring, and First-Person Stability in Inflectional Language Models17 Jul 2026
- 2026Graded Entity-Familiarity Readouts in Language Models: Polish Adaptation, Cross-Language Robustness, and Refusal Steering15 Jul 2026
- 2026The Reliability Gap in Benchmark Auditing: Distribution Shift and Scale as Failure Modes of Contamination Detection2 Jun 2026
- 2026What properties of reasoning supervision are associated with improved downstream model quality?13 May 2026
- 2026Long-Context Encoder Models for Polish Language Understanding12 Mar 2026
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