AI Coffee Break with Letitia: The video explains two main types of self-supervised learning: mask language modeling and autoregressive language modeling.
AI Coffee Break with Letitia - Only self-supervision
The video discusses two primary methods of self-supervised learning in natural language processing. The first method is mask language modeling, where 15% of tokens in a text are masked with a special token, and the model predicts these masked tokens. This approach is a classification task where the model selects from a vocabulary of around 60,000 tokens. Models using this method are known as Transformer encoders, with BERT and RoBERTa being notable examples. The second method is autoregressive language modeling, where the model completes the text by predicting the next word based on previous words. This method uses a cross-entropy loss function and is called autoregressive because the model uses its own output as input for subsequent predictions. The GPT family of models is a well-known example of this approach.
Key Points:
- Mask language modeling involves masking 15% of text tokens and predicting them, used by Transformer encoders like BERT.
- Autoregressive language modeling predicts the next word in a sequence, using its own output as input, exemplified by GPT models.
- Both methods use a cross-entropy loss function for training.
- Transformer encoders are typically identified by names like BERT or RoBERTa.
- Autoregressive models are known for their ability to generate coherent text sequences.
Details:
1. Understanding Masked Language Modeling 🕵️♂️
- Masked language modeling involves masking 15% of tokens in a text for prediction.
- The model operates in a classification setting by choosing from a vocabulary of approximately 60,000 tokens.
- Transformers that use masked language modeling are known as Transformer encoders.
- Notable examples of Transformer encoders include BERT and RoBERTa.
2. Exploring Auto-Regressive Language Modeling 🔄
- Auto-regressive language modeling uses a cross-entropy loss function to optimize text completion tasks, an approach that predicts the probability distribution of the next word in a sequence based on preceding words.
- The model's auto-regressive nature involves iterative processing where each generated word is used as input for subsequent word predictions, enhancing coherence and flow in generated text.
- A notable application of auto-regressive models is the GPT family, which effectively utilizes this approach to produce human-like text, demonstrating practical success in various language tasks.
- Understanding the cross-entropy loss function is crucial as it measures the difference between the predicted probability distribution and the actual distribution, guiding the model to minimize errors during training.
- Expanding on practical applications, auto-regressive models are used in chatbots, content creation, and translation services, showcasing their versatility and impact in real-world scenarios.
