arXiv:2605.28526v1 Announce Type: new Abstract: Masked language modeling has become a standard pretraining objective for training encoder-based language models. In this approach, certain tokens in the input are masked, and the model learns to predict them using the surrounding context. This process enables the model to capture both syntactic and semantic properties of language. Conventionally, the tokens selected for masking are chosen at random, which may not always yield the most effective learning signals. In this work, we examine a token masking strategy based on entropy distribution. We u
The paper, published in 2026, reflects ongoing academic efforts to improve the efficiency and effectiveness of large language model pretraining amid growing compute demands.
Improved masking strategies can lead to more efficient and capable language models, impacting the development and performance of AI applications across many sectors.
The conventional random token masking in language models may be superseded by more sophisticated, entropy-aware methods, leading to better model learning from the same data.
- · AI researchers
- · NLP developers
- · Cloud AI providers
More robust and generalizable encoder-based language models are developed with less computational overhead.
This efficiency gain could lower barriers to entry for developing advanced AI, potentially democratizing access to powerful models.
The enhanced model capabilities accelerate advancements in AI agents and other complex AI systems, expanding their application scope.
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Read at arXiv cs.AI