In this paper, the authors aim to demystify the performance gap between transformer-based language models and their ability to perform associative recall (AR), a task that involves recalling information from a context. The authors argue that this gap is surprising given the success of transformers in language modeling tasks. To address this gap, they propose several techniques to improve the AR capabilities of transformer-based models.
The authors begin by explaining that AR has a long history in machine learning and has been shown to be predictive of in-context learning quality. They then highlight the surprising performance gap between transformers and their ability to perform AR tasks. To address this gap, the authors propose several techniques, including:
- Gated convolution architectures: These are similar to attention mechanisms but operate on the entire input sequence rather than a fixed context window. This allows the model to capture longer-range dependencies and better understand the context in which the information is being recalled.
- Efficient transformers: The authors propose several techniques to improve the efficiency of transformer-based models, including weight pruning, knowledge distillation, and parallelization. By making these models more efficient, they can be used for longer AR tasks without sacrificing performance.
- Sparse modular activation: This technique involves representing words as a combination of sparse features, allowing the model to focus on the most important aspects of the input sequence when recalling information.
The authors evaluate their proposed techniques on several benchmark datasets and show that they can significantly improve the AR performance of transformer-based models. They also demonstrate that these improvements transfer to other language modeling tasks, such as language translation.
Overall, the paper provides a comprehensive analysis of the performance gap between transformers and their ability to perform AR tasks, and proposes several effective techniques to address this gap. By improving the AR capabilities of transformer-based models, these techniques have the potential to significantly improve the quality of language modeling systems in various applications.
