Tokenization and Visual Rendering: An In-Depth Analysis

Pixel-based language models aim to overcome the bottlenecks of tokenization by converting text into images. However, multimodal variants such as DualGPT reintroduce text tokenizers to improve autoregressive deliveries. A recent study focuses on four Indonesian local languages (Javanese, Balinese, Sundanese, and Lampungnese), evaluating the impact of script-tokenizer alignment within the DualGPT architecture.

Results and Implications

The research shows that, despite visual rendering, integrating a text tokenizer reintroduces the misalignment problem that pixel-based models seek to solve. Unexpectedly, the Llama 2 tokenizer, despite having lower OOV (out-of-vocabulary) and fertility rates, performs significantly worse than a custom tokenizer, with improvements of up to 30.15 chrF++ obtained with the latter. These results serve as a warning for the future development of multimodal variants, as text tokenizers remain a significant barrier to equitable models.

For those evaluating on-premise deployment, there are trade-offs between using pre-trained models and the need for custom training to optimize performance in specific scenarios. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these trade-offs.