Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models
arXiv:2303.15619v2 Announce Type: replace Abstract: The choice of \emph{which} tokens to mask is a central, under-examined design decision in masked language modeling (MLM). Standard pretraining masks tokens uniformly at random, but several studies show that more informative masking targets can improve downstream performance. We study masking as a \emph{task-adaptive} component of the fine-tuning pipeline and introduce \textbf{Typhoon}, a masking strategy that uses the gradient of the task loss with respect to one-hot token inputs to estimate, online, how much each token type contributes to th
The continuous evolution of large language models necessitates ongoing research into fundamental pre-training techniques, with efficiency and performance gains being critical for broad adoption.
Improved masking strategies can lead to more efficient and powerful pre-trained language models, impacting the quality and cost of AI applications across various industries.
The optimization of language model pre-training can lead to faster development cycles and potentially reduced computational costs for achieving state-of-the-art performance.
- · AI researchers
- · NLP application developers
- · Cloud AI providers
- · Enterprises adopting AI
- · Inefficient model architectures
- · High compute cost operations
More accurate and efficient language models become available.
Reduced barriers to entry for developing complex AI applications due to lower computational overhead.
Acceleration of research into autonomous AI agents as foundational models become more robust and adaptable.
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