This guide explains the "Latent Manipulator," an experimental AI architecture designed to "think" in a latent space before generating text, contrasting with standard Transformer models that predict text sequentially. It includes the theory, code for implementation, and links to datasets and pretrained model checkpoints.
Based on the video exploring this concept: [https://www.youtube.com/watch?v=fWiieyG2zes]
| This guide explains the "Latent Manipulator," an experimental AI architecture designed to "think" in a latent space before generating text, contrasting with standard Transformer models that predict text sequentially. It includes the theory, code for implementation, and links to resources. | |
| Based on the video exploring this concept: [https://www.youtube.com/watch?v=fWiieyG2zes] | |
| ## Why a Latent Manipulator? The Theory | |
| Standard Large Language Models (LLMs) like ChatGPT are typically based on the **Transformer** architecture. Their core operation involves predicting the *next word* (or token) in a sequence, given all the preceding words. This means their process of "thinking" or reasoning is intertwined with the act of generating text word-by-word. If you ask ChatGPT, "Can you think quietly before writing?", it might say yes, but architecturally, it *can't* – its computation *is* the generation process. | |
| Humans, however, can often form an "idea" or grasp the semantics of a concept before finding the exact words |
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