Positional Encodings & Long-Context Modeling
Transformers, unlike traditional recurrent or convolutional neural networks, process all tokens in a sequence simultaneously rather than in order. This parallelism means that transformers have no inherent sense of the order in which tokens appear. However, understanding the order of tokens is crucial for tasks like language modeling, where the meaning of a sentence can depend entirely on word order. To address this, transformers incorporate positional information by adding positional encodings to the input token embeddings. These encodings inject information about each token's position within the sequence, allowing the model to differentiate between, for instance, dog bites man and man bites dog. This approach enables the self-attention mechanism to consider both the content and the position of each token when computing relationships across the sequence.
- Use fixed, mathematically defined sine and cosine functions of different frequencies;
- Provide a unique pattern for each position, allowing the model to extrapolate to longer sequences than seen during training;
- Require no learned parameters, resulting in a lightweight and deterministic encoding;
- Commonly used in original transformer models where generalization to unseen sequence lengths is important.
- Use trainable vectors associated with each possible position in the input sequence;
- Allow the model to adapt position representations to the specific task and data distribution;
- Offer greater flexibility but may not generalize well to sequences longer than those seen during training;
- Often chosen when the training and inference sequence lengths are fixed and known in advance.
A context window is the maximum number of tokens a transformer model can process in a single forward pass. The size of this window determines how much of the input sequence the model can attend to at once. In long-context modeling, a larger context window enables the model to capture dependencies and relationships that span greater distances within the input, which is crucial for tasks like document summarization or code analysis. However, increasing the context window typically requires more memory and computation.
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Can you explain how positional encodings are generated in transformers?
Why can't transformers just use the order of input tokens directly?
Are there different types of positional encodings used in practice?
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Transformers, unlike traditional recurrent or convolutional neural networks, process all tokens in a sequence simultaneously rather than in order. This parallelism means that transformers have no inherent sense of the order in which tokens appear. However, understanding the order of tokens is crucial for tasks like language modeling, where the meaning of a sentence can depend entirely on word order. To address this, transformers incorporate positional information by adding positional encodings to the input token embeddings. These encodings inject information about each token's position within the sequence, allowing the model to differentiate between, for instance, dog bites man and man bites dog. This approach enables the self-attention mechanism to consider both the content and the position of each token when computing relationships across the sequence.
- Use fixed, mathematically defined sine and cosine functions of different frequencies;
- Provide a unique pattern for each position, allowing the model to extrapolate to longer sequences than seen during training;
- Require no learned parameters, resulting in a lightweight and deterministic encoding;
- Commonly used in original transformer models where generalization to unseen sequence lengths is important.
- Use trainable vectors associated with each possible position in the input sequence;
- Allow the model to adapt position representations to the specific task and data distribution;
- Offer greater flexibility but may not generalize well to sequences longer than those seen during training;
- Often chosen when the training and inference sequence lengths are fixed and known in advance.
A context window is the maximum number of tokens a transformer model can process in a single forward pass. The size of this window determines how much of the input sequence the model can attend to at once. In long-context modeling, a larger context window enables the model to capture dependencies and relationships that span greater distances within the input, which is crucial for tasks like document summarization or code analysis. However, increasing the context window typically requires more memory and computation.
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