Masked Language Model
A pre-training approach where the model learns to predict randomly hidden (masked) words in a sentence, building deep understanding of language structure and semantics.
Masked language modeling is the training objective behind BERT (Bidirectional Encoder Representations from Transformers), introduced by Google in 2018. During training, 15% of tokens are randomly masked and the model predicts them from context. This bidirectional approach — reading both left and right context — gave BERT breakthrough performance on NLP benchmarks, improving state-of-the-art results on 11 tasks simultaneously. The technique remains the foundation of encoder-based models used for classification, search, and information extraction.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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