How do LLMs learn contextual embeddings?

Large Language Models (LLMs) learn contextual embeddings through a process that involves deep neural networks, primarily using architectures like transformers. Contextual embeddings are vector representations of words or tokens that capture their meanings relative to the surrounding text. Unlike static embeddings (e.g., Word2Vec or GloVe), contextual embeddings adapt based on sentence structure, word order, and semantics.

The process begins during pretraining, where the model learns to predict missing or next words using massive text corpora. For instance, in the transformer-based model architecture like BERT or GPT, self-attention mechanisms allow the model to weigh the relevance of each word in a sequence with respect to all other words. This mechanism helps in capturing nuanced relationships, such as polysemy (a word with multiple meanings) and syntactic dependencies.

Each layer in a transformer refines the embeddings. At the first layer, a word is encoded with basic information. As it passes through subsequent layers, the model incorporates broader context—like subject-verb agreement, idioms, or long-distance dependencies—into the embedding. The final contextual embedding is thus a rich, dynamic representation that changes depending on how the word is used in the sentence.

For example, the word “bank” will have different embeddings in “river bank” versus “money in the bank,” as the model distinguishes meaning based on context.

These learned embeddings enable downstream tasks like sentiment analysis, translation, summarization, and text generation with remarkable accuracy. They are crucial for making generative models produce coherent and relevant content.

To deeply understand how LLMs acquire and use contextual embeddings, it’s essential to explore attention mechanisms, tokenization techniques, and training objectives—all of which are key components in a Generative AI and machine learning course by The IoT Academy.

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