@ahmadmanga's thread

Diffusion Process in LCM

• 🔍 The diffusion process is used to refine the embeddings, making the model learn to handle uncertainty and noise in the data.
• 🔄 The process involves adding noise to the sentence embedding and then using the Transformer to predict the noise, refining the embedding through multiple steps.
• 📈 The diffusion process enables the model to explore multiple potential meanings or refinements of a specific presentation, adding robustness and flexibility.

Limitations of LCM

• 🚫 Sentences in the Sona space remain discrete combinatorial objects, despite being represented as continuous vectors.
• 📊 The choice and design of the embedding space play a crucial role in the LCM approach, and if not optimally built, the system may fail.
• 📚 The Sona model was trained on a specific dataset with short sentences, which may not be suitable for more complex topics or longer sentences.