In [[transformer model|transformer models]], an attention score represents the relevance or importance of one sequence element (such as a word) with respect to another element. These attention scores are calculated using the [[dot product]] or other similarity measures between pairs of [[query]], [[key]], and [[value]] vectors. Attention scores are a critical component of the [[self-attention]] mechanism, which is fundamental to the functioning of [[transformer model|transformers]]. In the context of [[transformer model|transformer models]], attention scores are calculated as follows: 1. **[[query]], [[key]], and [[value]] vectors:** - Each sequence element (e.g., a word) is associated with three types of vectors: [[query]], key, and value vectors. - These vectors are derived from the input embeddings of the sequence elements and are linearly transformed using learned weight matrices. 2. **Calculating Attention Scores:** - For each query vector, the model calculates a set of attention scores by taking the dot product (or other similarity measures) between the query vector and all key vectors in the sequence. - The dot product measures the similarity or compatibility between the query and each key. - The higher the dot product (or the higher the similarity), the higher the attention score will be. 3. **Softmax and Attention Weights:** - The calculated attention scores are often transformed using the softmax function to obtain a probability distribution over all key vectors. - Softmax converts the raw scores into a set of attention weights that sum up to 1. - These attention weights reflect the relative importance of each key with respect to the query. 4. **Weighted Sum of Values:** - The attention weights obtained from the softmax are used to compute a weighted sum of the corresponding value vectors. - The weighted sum is a combination of the values, where more weight is given to the values associated with higher attention scores. - This weighted sum represents the contribution of different sequence elements to the output for the specific query. In summary, attention scores quantify how much attention a specific element (query) should give to other elements (keys) in the sequence. These scores are computed by assessing the similarity between the query and key vectors. The attention mechanism then generates a weighted sum of value vectors based on these scores, allowing the model to focus on relevant information and capture relationships between elements at varying distances in the sequence. This self-attention mechanism is crucial for the transformer architecture's ability to handle sequential data effectively. [[value]] < [[Hands-on LLMs]]/[[2 LLMs and Transformers]] > [[dot product]]