--- title: "Self-Attention and Multi-Head Attention in Large Language Models (LLMs)" description: "this blog will help you to understand about the Self- attention and multi-head attention in LLMs." author: "Yash Srivastava" published: 2026-06-16 updated: 2026-06-16 canonical: https://answers.mindstick.com/blog/407/self-attention-and-multi-head-attention-in-large-language-models-llms category: "technology" tags: ["llm", "ai model", "attention mechanism"] reading_time: 4 minutes --- # Self-Attention and Multi-Head Attention in Large Language Models (LLMs) ## Introduction [Attention Mechanism](https://answers.mindstick.com/blog/406/attention-mechanism-in-llms) completely transform the concept of [Natural Language Processing](https://www.mindstick.com/articles/33695/natural-language-processing) ([NLP](https://www.mindstick.com/articles/335424/natural-language-processing-nlp-in-ai)) and Large [Language Model](https://www.mindstick.com/interview/34471/what-is-a-large-language-model-llm) (LLM). Attention provides capability to a model such that they can find the relationship between the different words in a sentence and gather the information with [understanding](https://www.mindstick.com/articles/12918/cat-5e-vs-cat-6a-understanding-the-major-differences) of context.\ But modern Transformers do not use Attention only. The actual core component of Transformer is Self-attention and Multi-Head Attention. Self-Attention provides the ability to the model such that they can compare token of a sentence with the other tokens of the same sentence whereas multi-head attention provides capability to understand the sentence from different perspectives. ## What is Self-Attention? Self-Attention is a mechanism in which current token of any sentence analyze the other tokens of the same sentence and decides which token is more focused to understand the context of the current token. *Example:* ```plaintext The animal didn't cross the street because it was tired. ``` Here when the model will process **it,** then model will check the whole sentence and then find the possible [relationships](https://yourviews.mindstick.com/view/82648/the-importance-of-unimportant-relationships) like: ```plaintext animal cross street tired ``` Then model will calculate: ```plaintext animal → High Importance street → Low Importance cross → Medium Importance ``` and create the conclusion like: ```plaintext it = animal ``` This is the basic Idea of Self-attention. ## Why is it Called Self-Attention? The word "Self" means sentence is attending itself. *Example:* ```plaintext I love learning Artificial Intelligence. ``` here: ```plaintext I love learning Artificial Intelligence ``` All the words can communicate with each other. so, sentence attends to itself. ## ## Self-Attention Flow ```plaintext Input Tokens ↓ Embeddings ↓ Query, Key, Value ↓ Attention Scores ↓ Softmax ↓ Weighted Values ↓ Context-Aware Representation ``` This process is repeated for each token. ## Why Self-Attention is Powerful? Before Self-Attention the word have to be understand individually but after Self-Attention the word is being understand with the context of the sentence. *Example:* ```plaintext Sentence 1: I deposited money in the bank. Sentence 2: I sat near the river bank. ``` **Word:** bank, is same but the Self-attention will check the nearby words like: ```plaintext money, deposited and understand Financial Bank and river, near and understand River Bank ``` ## Limitation of Single Self-Attention A question can arise if Self-attention is much powerful then why we need Multi-head attention. The reason is that the Single self-attention Layer focuses on a single type of relation. *Example sentence:* ```plaintext The boy who studied hard passed the exam. ``` Sentence have multiple relationships: ```plaintext boy ↔ studied boy ↔ passed hard ↔ studied exam ↔ passed ``` A single attention mechanism can not capture the all relationships perfectly. ## What is Multi-Head Attention? The Multi-Head attention idea is generated by using multiple attentions instead of using single attemtion layer. *Example:* ```plaintext Head 1 Head 2 Head 3 Head 4 ``` Each head will analyze the sentence with different perspectives. ## ## Different Heads Learn Different Things *Example:* ```plaintext The boy who studied hard passed the exam. Head 1: Grammar Relationship Head 2: Subject-Verb Relationship Head 3: Long Distance Dependency Head 4: Semantic Meaning ``` each head learns different patterns. ## Multi-Head Attention Architecture ```plaintext Input Embeddings ↓ Split into Heads ↓ Head 1 Attention Head 2 Attention Head 3 Attention Head 4 Attention ↓ Concatenate ↓ Linear Layer ↓ Output ``` ## Example ```plaintext Sentence: The capital of France is Paris. Head 1: capital ↔ Paris Head 2: France ↔ Paris Head 3: The ↔ capital Head 4: Sentence Structure ``` Every heads generates richer relationship collectively. ## Why Multi-Head Attention is Important? Benefits: - Better Context Understanding - Better Long-Range Dependencies - Better Semantic Understanding - Better Reasoning ## Self-Attention vs Multi-Head Attention | Self-Attention | Multi-Head Attention | | --- | --- | | Single attention mechanism | Multiple attention mechanisms | | One perspective | Multiple perspectives | | Limited pattern capture | Rich pattern capture | | Simpler | More powerful | | Lower computation | Higher computation | ## Where is Multi-Head Attention Used? Almost every modern LLM: - OpenAI GPT Models - Meta LLaMA - [Google Gemini](https://yourviews.mindstick.com/view/88395/chatgpt-vs-google-gemini-vs-copilot-which-ai-is-best) - [Mistral AI](https://yourviews.mindstick.com/view/86046/mistral-ai-launches-open-source-llm-mistral-7b) Mistral - Anthropic Claude uses Multi-Head Attention. --- Original Source: https://answers.mindstick.com/blog/407/self-attention-and-multi-head-attention-in-large-language-models-llms Copyright © MindStick Software Pvt. Ltd. This Markdown version is provided for developers, AI systems, and offline reading.