Improving Length-Generalization in Transformers via Task Hinting
Pranjal Awasthi Anupam Gupta
It has been observed in recent years that transformers have problems with
length generalization for certain types of reasoning and arithmetic tasks. In
particular, the performance of a transformer model trained on tasks (say
addition) up to a certain length (e.g., 5 digit numbers) drops sharply when
applied to longer instances of the same problem. This work proposes an approach
based on task hinting towards addressing length generalization. Our key idea is
that while training the model on task-specific data, it is helpful to
simultaneously train the model to solve a simpler but related auxiliary task as
well.
  We study the classical sorting problem as a canonical example to evaluate our
approach. We design a multitask training framework and show that task hinting
significantly improve length generalization. For sorting we show that it is
possible to train models on data consisting of sequences having length at most
$20$, and improve the test accuracy on sequences of length $100$ from less than
1% (for standard training) to more than 92% (via task hinting).
  Our study uncovers several interesting aspects of length generalization. We
observe that while several auxiliary tasks may seem natural a priori, their
effectiveness in improving length generalization differs dramatically. We
further use probing and visualization-based techniques to understand the
internal mechanisms via which the model performs the task, and propose a
theoretical construction consistent with the observed learning behaviors of the
model. Based on our construction, we show that introducing a small number of
length dependent parameters into the training procedure can further boost the
performance on unseen lengths. Finally, we also show the efficacy of our task
hinting based approach beyond sorting, giving hope that these techniques will
be applicable in broader contexts.