Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation (2410.18565v1)

Abstract: We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in LLM development through innovative techniques. These include Weighted Instruction Cross-Entropy Loss, which balances the learning of different instruction types, and Adaptive Learning Rate, which dynamically adjusts the learning rate based on training progress. To evaluate performance, we created the Open PL LLM Leaderboard and Polish MT-Bench, novel frameworks assessing various NLP tasks and conversational abilities. Bielik 7B v0.1 demonstrates significant improvements, achieving a 9 percentage point increase in average score compared to Mistral-7B-v0.1 on the RAG Reader task. It also excels in the Polish MT-Bench, particularly in Reasoning (6.15/10) and Role-playing (7.83/10) categories. This model represents a substantial advancement in Polish language AI, offering a powerful tool for diverse linguistic applications and setting new benchmarks in the field.

• The paper introduces a cutting-edge 7B-parameter model that leverages weighted instruction cross-entropy loss and adaptive learning rates for efficient training.
• It demonstrates enhanced performance on Polish NLP benchmarks, outperforming its predecessor by nearly 9 percentage points on the RAG Reader task.
• The research sets a precedent for future AI in underrepresented languages while outlining advancements in tokenization and ethical considerations.

Overview of Bielik 7B v0.1: A Polish LLM

The development of large-scale LLMs has traditionally focused on extensively resourced languages like English, marginalizing those with fewer digital resources. The paper "Bielik 7B v0.1: A Polish LLM – Development, Insights, and Evaluation" addresses this gap by introducing a generative text model specifically designed for Polish. This 7-billion-parameter model leverages distinct techniques, demonstrates notable performance on Polish NLP tasks, and establishes a framework for future advancements in non-English AI research.

Development and Techniques

Bielik 7B v0.1 builds upon the foundation of the Mistral 7B v0.1 model. The authors highlight several critical innovations:

• Weighted Instruction Cross-Entropy Loss and Adaptive Learning Rate techniques are deployed to optimize model training. These strategies ensure balanced learning and efficient convergence by adjusting the learning rate dynamically.
• A diverse dataset, primarily composed of Polish texts, was curated. This dataset underwent rigorous preprocessing and quality evaluation, yielding a robust training corpus of 22 billion tokens (supplemented with 14 billion English tokens).

Model Architecture

The architecture of Bielik 7B v0.1 follows a Transformer-based design with notable configurations such as 32 layers and 32 attention heads. The model integrates advanced features like Rotary Positional Embeddings, SwiGLU activation, and Root Mean Square Layer Normalization, which collectively enhance performance on Polish language processing.

Tokenization

While relying on the Mistral 7B model's tokenizer, the authors attempted to expand and refine it to better suit Polish syntax and morphology. Tokenization efficiency was evaluated through metrics like tokens per word and characters per token, although issues with incorrect token combinations were acknowledged.

Evaluation and Results

Performance evaluations were conducted through two primary frameworks: the Open PL LLM Leaderboard and the Polish MT-Bench.

1. Open PL LLM Leaderboard: Bielik 7B v0.1 outperformed its predecessor (Mistral-7B-v0.1) in the RAG Reader task by almost 9 percentage points, demonstrating competitive scores on various NLP benchmarks (e.g., sentiment analysis, named entity recognition).
2. Polish MT-Bench: In this conversational and instruction-following evaluation, Bielik 7B v0.1 was effective particularly in reasoning and role-playing tasks, reflecting its comprehensive conversational abilities.

Implications and Future Directions

Bielik 7B v0.1 marks a significant stride in Polish AI, providing a compelling resource for NLP tasks in less-resourced languages. While the model sets new benchmarks, its development invites further exploration into:

• Broader Linguistic Application: Extending techniques to other underrepresented languages.
• Ethical Considerations: Approaches to mitigate bias and misinformation inherent to models trained on expansive web-crawled data.
• Computational Efficiency: Techniques like quantization and calibration ensure the model remains accessible for limited-resource environments, fostering its use in practical applications.

Conclusion

This research underscores the potential of leveraging advanced ML techniques to empower LLMs in diverse linguistic contexts. While not revolutionary in a global AI sense, Bielik 7B v0.1 distinctly enhances Polish NLP, offering a foundational step toward inclusive AI technologies. Future iterations could see more efficient tokenization methods and enriched datasets to further enhance both scope and efficacy of the model's outputs.