RobBERT: a Dutch RoBERTa-based Language Model (2001.06286v2)

Abstract: Pre-trained LLMs have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural language tasks. One of the most prominent pre-trained LLMs is BERT, which was released as an English as well as a multilingual version. Although multilingual BERT performs well on many tasks, recent studies show that BERT models trained on a single language significantly outperform the multilingual version. Training a Dutch BERT model thus has a lot of potential for a wide range of Dutch NLP tasks. While previous approaches have used earlier implementations of BERT to train a Dutch version of BERT, we used RoBERTa, a robustly optimized BERT approach, to train a Dutch LLM called RobBERT. We measured its performance on various tasks as well as the importance of the fine-tuning dataset size. We also evaluated the importance of language-specific tokenizers and the model's fairness. We found that RobBERT improves state-of-the-art results for various tasks, and especially significantly outperforms other models when dealing with smaller datasets. These results indicate that it is a powerful pre-trained model for a large variety of Dutch language tasks. The pre-trained and fine-tuned models are publicly available to support further downstream Dutch NLP applications.

Overview of RobBERT: A Dutch RoBERTa-based LLM

The paper presents RobBERT, a Dutch LLM based on the RoBERTa architecture, demonstrating its superiority over existing Dutch models, especially in scenarios with limited data availability. This paper represents a significant step forward in the specialization of pre-trained NLP models for non-English languages, addressing the nascent yet crucial problem of linguistic diversity in NLP.

RobBERT was developed using the RoBERTa training framework, which refined the original BERT model by optimizing pre-training procedures, particularly by discarding the Next Sentence Prediction task. The authors introduce two versions of RobBERT, evaluating the importance of a language-specific tokenizer. The Dutch-specific tokenizer in the second version yielded notable improvements, thereby stressing the importance of language specificity in tokenization for model performance.

Methodology

The authors trained RobBERT using the OSCAR corpus, a considerably large multilingual corpus that provided an extensive dataset to refine the model. The choice of this corpus reaffirms the potential of larger data sources in achieving cutting-edge model performance. The pre-training process adhered closely to RoBERTa's methodology, involving masked LLMing (MLM), and made use of a computationally efficient training infrastructure.

RobBERT's architecture, consisting of 12 self-attention layers and being congruent with the RoBERTa base model, positions it ahead of previous models concerning contextual understanding and generalizability across various Dutch linguistic tasks. Pre-training involved two epochs with a significant batch size, leveraging extensive computational resources to ensure robust model development.

Evaluation and Results

The evaluation of RobBERT spans multiple Dutch-specific tasks, including sentiment analysis and grammatical disambiguation (die/dat disambiguation), as well as token-level tasks like part-of-speech tagging and named entity recognition (NER). In sentiment analysis, RobBERT outperformed its multilingual and Dutch counterparts, particularly excelling in datasets where training examples were scarce. This highlights RobBERT’s effectiveness in low-resource scenarios, a particularly valuable attribute for languages with fewer linguistic resources.

The die/dat disambiguation task emphasized RobBERT's ability to manage grammatical subtleties, achieving superior results in zero-shot scenarios—a testament to its strong pre-training phase. In token-level tasks, RobBERT exhibited slight improvements over existing models, signaling its capacity to engage with Dutch linguistic structure effectively.

A further dimension of their analysis explored fairness in LLMs. By examining gender stereotypes and predictive disparities in downstream tasks, the paper underscores a growing concern in NLP applications—representational harm. Although models like RobBERT show promising results, ongoing research into algorithmic fairness remains imperative.

Implications and Future Directions

RobBERT sets a new benchmark for Dutch LLMs, presenting itself as a valuable resource for both academic inquiries and practical applications in NLP. It opens avenues for more precise and effective Dutch NLP systems, enabling advancements in areas such as machine translation, sentiment analysis, and automated content generation within Dutch-speaking regions.

This work implies a broader trend towards creating specialized LLMs tailored for specific languages, asserting that pre-training on language-specific corpora yields significant advancements over multilingual approaches. Additionally, the implications of integrating tokenization tailored to the linguistic peculiarities of a language are profound, suggesting a worthwhile direction for future research.

The paper suggests several enhancements, including improvements to the pre-training data's preparation and considering morphological word structures in tokenization. With fairness a critical component of model evaluation, further work is encouraged to ensure equitable predictive performance across demographic lines.

Conclusively, RobBERT not only addresses the practical demands for specialized Dutch NLP tools but also contributes to the ongoing discourse surrounding the ethical deployment of AI technologies. As LLMs continue to evolve, it will be crucial to balance performance improvements with ethical considerations, ensuring that such advancements serve all users equitably.