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Auto Regressive Prediction vs. Diffusion Model in Text Generation
Auto regressive prediction in language models involves predicting one token at a time, akin to individual computations needed for the model to 'think.' This method mirrors the process of writing but may not align with how we actually think. On the other hand, the diffusion model generates the entire text sequence at once, although it is less popular due to its lower performance. The diffusion model creates a coherent sequence by denoising the generated text, mirroring a more holistic thought process akin to how our brains arrive at thoughts.