arXiv:2606.14122v1 Announce Type: new Abstract: Byte-level tokenization enables language models to handle any Unicode input, but models can generate invalid UTF-8 sequences when encountering rare or unseen characters. We investigate the relationship between training scale and UTF-8 generation reliability with a 355M parameter model trained on 80B tokens from a balanced multilingual corpus of English, Japanese, Korean, and Chinese. We introduce multiple evaluation protocols that isolate UTF-8 structural validity from language modeling. UTF-8 validity convergence lags perplexity by a roughly a f
The proliferation of byte-aware language models necessitates deeper investigation into their foundational reliability, particularly regarding encoding standards.
Ensuring the integrity of generated text data, especially in multilingual contexts, is crucial for the dependable operation and deployment of advanced AI systems.
This research highlights a new frontier in language model evaluation beyond traditional perplexity, focusing on fundamental data validity that impacts downstream applications.
- · AI Safety Researchers
- · Multilingual AI Developers
- · Model Evaluation Platforms
- · Models with poor UTF-8 handling
Language model development will increasingly integrate strict UTF-8 validity metrics alongside linguistic quality.
Improved model reliability will broaden the functional applicability of AI in diverse global linguistic markets.
Standards for 'deployable' AI models will evolve to include minimum UTF-8 validity benchmarks, influencing regulatory frameworks.
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Read at arXiv cs.CL