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An ideal length-extrapolatable Transformer language model can handlesequences longer than the training length without any fine-tuning. Suchlong-context utilization capability relies heavily on a flexible positionalembedding design. Upon investigating the flexibility of existing largepre-trained Transformer language models, we find that the T5 family deserves acloser look, as its positional embeddings capture rich and flexible attentionpatterns. However, T5 suffers from the dispersed attention issue: the longerthe input sequence, the flatter the attention distribution. To alleviate theissue, we propose two attention alignment strategies via temperature scaling.Our findings show improvement on the long-context utilization capability of T5on language modeling, retrieval, multi-document question answering, and codecompletion tasks without any fine-tuning. This suggests that a flexiblepositional embedding design and attention alignment can go a long way towardTransformer length extrapolation.

Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation