Dutch Language Model

Research on Dutch language models focuses on improving their performance and addressing specific linguistic challenges, such as handling gender-neutral pronouns and adapting to evolving language use. Current efforts involve fine-tuning existing large language models like Llama 2 and RoBERTa, employing techniques like counterfactual data augmentation to mitigate biases, and developing comprehensive benchmarks like DUMB to evaluate model performance across diverse tasks. These advancements are crucial for enhancing natural language processing capabilities in Dutch, impacting applications ranging from automated speech recognition to sentiment analysis and information retrieval.