LPC-SM: Local Predictive Coding and Sparse Memory for Long-Context Language Modeling

1. 1Separate local attention and persistent memory using Orthogonal Novelty Transport (ONT)
2. 2Implement predictive correction and run-time control within the same block
3. 3Use LPC-SM to handle long-range state and local interaction in sequence modeling
4. 4Evaluate the performance of LPC-SM against traditional attention-based models

NLP engineers and researchers on a team can benefit from LPC-SM as it provides an alternative approach to traditional attention-based models, allowing for more efficient and effective long-context language modeling

💡 LPC-SM provides an alternative decomposition of sequence modeling that can be more efficient and effective than traditional attention-based models

🤖 LPC-SM: A new hybrid autoregressive architecture for long-context language modeling #LLMs #NLP