Latent Semantic Spaces and Prompt Activation
Understanding how large language models (LLMs) generalize to new tasks without explicit training data requires you to grasp the concept of latent semantic spaces. These are high-dimensional vector spaces where LLMs encode their knowledge. Each token, phrase, or even abstract concept is mapped to a unique point or region in this space. The relationships between these points capture semantic similarity, analogy, and even logical structure. When you input a prompt, the model interprets it as a trajectory through this latent space, effectively "activating" regions that correspond to relevant knowledge or reasoning patterns. The prompt does not inject new information, but rather guides the model to retrieve and combine existing representations in novel ways.
In latent semantic spaces, concepts can be combined or transformed using vector arithmetic. For example, the vector difference between "king" and "man" added to "woman" often points toward "queen". This geometric property allows LLMs to perform analogical reasoning and compositional generalization.
When you provide a prompt, you are conditioning the model's output distribution on the context you specify. This is analogous to selecting a subspace within the larger latent space, where the model's probability mass is concentrated on knowledge relevant to your prompt.
Prompts act as coordinates or directions in this high-dimensional space, steering the model toward regions where relevant knowledge is densely encoded. The geometry of these spaces enables efficient retrieval and recombination of information for zero-shot generalization.
Prompting is not a mechanism for teaching the model new information. Instead, it serves as a tool for selecting and activating subspaces of pre-existing knowledge within the model's latent semantic space.
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Understanding how large language models (LLMs) generalize to new tasks without explicit training data requires you to grasp the concept of latent semantic spaces. These are high-dimensional vector spaces where LLMs encode their knowledge. Each token, phrase, or even abstract concept is mapped to a unique point or region in this space. The relationships between these points capture semantic similarity, analogy, and even logical structure. When you input a prompt, the model interprets it as a trajectory through this latent space, effectively "activating" regions that correspond to relevant knowledge or reasoning patterns. The prompt does not inject new information, but rather guides the model to retrieve and combine existing representations in novel ways.
In latent semantic spaces, concepts can be combined or transformed using vector arithmetic. For example, the vector difference between "king" and "man" added to "woman" often points toward "queen". This geometric property allows LLMs to perform analogical reasoning and compositional generalization.
When you provide a prompt, you are conditioning the model's output distribution on the context you specify. This is analogous to selecting a subspace within the larger latent space, where the model's probability mass is concentrated on knowledge relevant to your prompt.
Prompts act as coordinates or directions in this high-dimensional space, steering the model toward regions where relevant knowledge is densely encoded. The geometry of these spaces enables efficient retrieval and recombination of information for zero-shot generalization.
Prompting is not a mechanism for teaching the model new information. Instead, it serves as a tool for selecting and activating subspaces of pre-existing knowledge within the model's latent semantic space.
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