# An Evolved Universal Transformer Memory

**Authors:** Edoardo Cetin, Qi Sun, Tianyu Zhao, Yujin Tang  
**arXiv:** [2410.13166](https://arxiv.org/abs/2410.13166)  
**Date:** 2024-10-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2410.13166v1/

## Key points

- Authors address the challenge of managing escalating costs associated with modern foundation models
- Traditional methods involve selectively dropping parts of the model's context using hand-designed rules while maintaining performance levels
- Neural Attention Memory Models (NAMMs) are introduced as a solution to this trade-off, enhancing efficiency and performance of transformers
- NAMMs incorporate a learned network for memory management that focuses on extracting relevant information for individual layers and attention heads
- Training NAMMs on a limited set of problems leads to significant performance enhancements across multiple benchmarks requiring long-context comprehension
- NAMMs demonstrate versatility in facilitating zero-shot transfer learning across diverse transformer architectures and input modalities
- Benefits of NAMMs extend beyond language tasks to encompass vision-related challenges and reinforcement learning scenarios

## Summary

In their paper titled "An Evolved Universal Transformer Memory," authors Edoardo Cetin, Qi Sun, Tianyu Zhao, and Yujin Tang address the challenge of managing the escalating costs associated with modern foundation models. Traditional methods have attempted to mitigate these costs by selectively dropping parts of the model's context using hand-designed rules while striving to maintain performance levels. However, the authors introduce Neural Attention Memory Models (NAMMs) as a solution to this trade-off. NAMMs incorporate a learned network for memory management that enhances both the efficiency and performance of transformers. By building upon pre-trained transformers, NAMMs offer various latent contexts that focus on extracting the most relevant information for individual layers and attention heads. This approach proves to be universally applicable to any model utilizing self-attention, as it conditions solely on the values within the attention matrices generated during processing. Through training NAMMs on a limited set of problems, significant performance enhancements are achieved across multiple benchmarks requiring long-context comprehension. Remarkably, these improvements are accompanied by a reduction in input context sizes compared to original configurations. The authors demonstrate the versatility of NAMMs by showcasing their ability to facilitate zero-shot transfer learning across diverse transformer architectures and input modalities. Notably, the benefits of NAMMs extend beyond language tasks to encompass vision-related challenges and reinforcement learning scenarios. The findings presented in this study underscore the potential of Neural Attention Memory Models in revolutionizing how transformers manage information flow and optimize performance across various domains.