In recent years, the NLP community has seen significant progress in research thanks to pretrained language models. These models can be fine-tuned or prompted to perform various tasks, and their quality tends to improve as the model size increases. However, this comes at the cost of increased computational requirements, especially for longer sequences. An alternative approach is to recompute all previous tokens on each inference step, storing only a single set of keys and values at a time. This method is more efficient than offloaded caching, especially for shorter sequences due to the overhead from loading and storing cache from RAM or SSD.
Attention Is All You Need
In this paper, Vaswani et al. (2017) proposed the Transformer model, which utilizes self-attention mechanisms to process input sequences efficiently. The authors showed that this approach allows for parallelization of attention computation, reducing computational requirements significantly. They also introduced the concept of multi-head attention, allowing the model to jointly attend to information from different representation subspaces at different positions. This design enables the model to capture complex contextual relationships in input sequences more effectively.
Fine-Tuning Language Models over Slow Networks
In this paper, Wang et al. (2022) explore the use of activation compression with guarantees for fine-tuning language models over slow networks. The authors propose a novel technique that compresses activations without losing accuracy, reducing the computational requirements of the model. They demonstrate that their approach achieves competitive performance on various NLP tasks while being more efficient than traditional fine-tuning methods.
Key Takeaways
- Pretrained language models have revolutionized NLP research by providing a solid foundation for many tasks.
- Attention mechanisms are key to processing input sequences efficiently in transformer models.
- Self-attention allows for parallelization of attention computation, reducing computational requirements.
- Multi-head attention captures complex contextual relationships in input sequences more effectively.
- Fine-tuning language models over slow networks using activation compression with guarantees can reduce computational requirements without sacrificing performance.
Conclusion
In conclusion, the articles discuss two important aspects of pretrained language models – their efficiency and effectiveness in processing input sequences. The Transformer model proposed by Vaswani et al. (2017) demonstrates that attention mechanisms are crucial for efficient sequence processing, while Wang et al. (2022) show that activation compression with guarantees can significantly reduce computational requirements for fine-tuning language models over slow networks. These findings have important implications for the development of more efficient and effective NLP models in the future.
