mlsanityData to 7 Oct 2026

Bielik 11B v2 Technical Report

arXiv 2505.02410 ↗First posted 5 May 2025cs.CL cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

We present Bielik 11B v2, a state-of-the-art language model optimized for Polish text processing. Built on the Mistral 7B v0.2 architecture and scaled to 11B parameters using depth up-scaling, this model demonstrates exceptional performance across Polish language benchmarks while maintaining strong cross-lingual capabilities. We introduce two key technical innovations: Weighted Instruction Cross-Entropy Loss, which optimizes learning across diverse instruction types by assigning quality-based weights to training examples, and Adaptive Learning Rate, which dynamically adjusts based on context length. Comprehensive evaluation across multiple benchmarks demonstrates that Bielik 11B v2 outperforms many larger models, including those with 2-6 times more parameters, and significantly surpasses other specialized Polish language models on tasks ranging from linguistic understanding to complex reasoning. The model's parameter efficiency and extensive quantization options enable deployment across various hardware configurations, advancing Polish language AI capabilities and establishing new benchmarks for resource-efficient language modeling in less-represented languages.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 56 references that are in the map. First posted on arXiv 5 May 2025.

Builds on

33 of 56 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  5. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
  6. 2024Qwen2 Technical Reportcited by 2,076
  7. 2020GLU Variants Improve Transformercited by 1,724
  8. 2022Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference timecited by 1,366
  9. 2023GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpointscited by 1,311
  10. 2024SimPO: Simple Preference Optimization with a Reference-Free Rewardcited by 951
  11. 2023TIES-Merging: Resolving Interference When Merging Modelscited by 795
  12. 2016Language Modeling with Gated Convolutional Networkscited by 688
21 more
  1. 2024ORPO: Monolithic Preference Optimization without Reference Modelcited by 556
  2. 2023The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variantscited by 300
  3. 2024Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positivecited by 239
  4. 2018Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learningcited by 211
  5. 2023SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scalingcited by 172
  6. 2024Simple and Scalable Strategies to Continually Pre-train Large Language Modelscited by 122
  7. 2024Model Stock: All we need is just a few fine-tuned modelscited by 87
  8. 2023EQ-Bench: An Emotional Intelligence Benchmark for Large Language Modelscited by 75
  9. 2024MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixturescited by 73
  10. 2022Continual Learning with Foundation Models: An Empirical Study of Latent Replaycited by 61
  11. 2020KLEJ: Comprehensive Benchmark for Polish Language Understandingcited by 51
  12. 2024Instruction Tuning With Loss Over Instructionscited by 40
  13. 2023Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformerscited by 30
  14. 2019Evaluation of Sentence Representations in Polishcited by 18
  15. 2023ScandEval: A Benchmark for Scandinavian Natural Language Processingcited by 16
  16. 2023StyloMetrix: An Open-Source Multilingual Tool for Representing Stylometric Vectorscited by 14
  17. 2024BenCzechMark : A Czech-centric Multitask and Multimetric Benchmark for Large Language Models with Duel Scoring Mechanismcited by 12
  18. 2022Training Effective Neural Sentence Encoders from Automatically Mined Paraphrasescited by 11
  19. 2024GPT-4 passes most of the 297 written Polish Board Certification Examinationscited by 10
  20. 2022PolQA: Polish Question Answering Datasetcited by 7
  21. 2025Evaluating Polish linguistic and cultural competency in large language modelscited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.