rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
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Ksenia Se
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15 types of attention mechanisms
Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention.
Here is a list of 15 types of attention mechanisms used in AI models:
1. Soft attention (Deterministic attention) -> https://huggingface.co/papers/1409.0473
Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1.
2. Hard attention (Stochastic attention) -> https://huggingface.co/papers/1508.04025
Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything.
3. Self-attention -> https://huggingface.co/papers/1706.03762
Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation.
4. Cross-Attention (Encoder-Decoder attention) -> https://huggingface.co/papers/2104.08771
The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources.
5. Multi-Head Attention (MHA) -> https://huggingface.co/papers/1706.03762
Multiple attention “heads” are run in parallel. The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values.
6. Multi-Head Latent Attention (MLA) -> https://huggingface.co/papers/2405.04434
Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations.
7. Memory-Based attention -> https://huggingface.co/papers/1503.08895
Involves an external memory and uses attention to read from and write to this memory.
See other types in the comments 👇
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about 15 hours ago
15 types of attention mechanisms
Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention.
Here is a list of 15 types of attention mechanisms used in AI models:
1. Soft attention (Deterministic attention) -> https://huggingface.co/papers/1409.0473
Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1.
2. Hard attention (Stochastic attention) -> https://huggingface.co/papers/1508.04025
Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything.
3. Self-attention -> https://huggingface.co/papers/1706.03762
Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation.
4. Cross-Attention (Encoder-Decoder attention) -> https://huggingface.co/papers/2104.08771
The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources.
5. Multi-Head Attention (MHA) -> https://huggingface.co/papers/1706.03762
Multiple attention “heads” are run in parallel. The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values.
6. Multi-Head Latent Attention (MLA) -> https://huggingface.co/papers/2405.04434
Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations.
7. Memory-Based attention -> https://huggingface.co/papers/1503.08895
Involves an external memory and uses attention to read from and write to this memory.
See other types in the comments 👇
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