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LLaDA: The Diffusion Model That Could Redefine Language Generation
- LLaDA introduces a novel approach to Language Generation by using a 'diffusion-based' process, moving away from traditional autoregression.
- LLaDA operates without the use of RLHF, which is common in current large language models (LLMs).
- Current LLMs, based on the Transformer architecture, predict text tokens one at a time using masked self-attention.
- LLaDA aims to address limitations of current LLMs, such as computational expense, lack of global reasoning, and reliance on vast training data.
- Rather than predicting subsequent tokens, LLaDA focuses on a diffusion-based generation process during pre-training.
- The 'remasking' concept in LLaDA allows for a more controlled and refined text generation process compared to autoregressive models.
- LLaDA combines diffusion with autoregressive generation in semi-autoregressive diffusion, offering a hybrid approach for language generation.
- Inspired by image diffusion models, LLaDA progressively unmasks tokens to generate coherent language, akin to 'denoising' in image generation.
- LLaDA shows promise in improving efficiency, reasoning, and context understanding in language models, with potential for diverse applications.
- The flexibility of LLaDA in adjusting parameters makes it suitable for various tasks, indicating a shift towards more natural and efficient language models.
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