Length generalization, the ability to generalize from small training context
sizes to larger ones, is a critical challenge in the development of
Transformer-based language models. Positional encoding (PE) has been identified
as a major factor influencing length generalization, but the exact impact of
different PE schemes on extrapolation in downstream tasks remains unclear. In
this paper, we conduct a systematic empirical study comparing the length
generalization performance of decoder-only Transformers with five different
position encoding approaches including Absolute Position Embedding (APE), T5's
Relative PE, ALiBi, and Rotary, in addition to Transformers without positional
encoding (NoPE). Our evaluation encompasses a battery of reasoning and
mathematical tasks. Our findings reveal that the most commonly used positional
encoding methods, such as ALiBi, Rotary, and APE, are not well suited for
length generalization in downstream tasks. More importantly, NoPE outperforms
other explicit positional encoding methods while requiring no additional
computation. We theoretically demonstrate that NoPE can represent both absolute
and relative PEs, but when trained with SGD, it mostly resembles T5's relative
PE attention patterns. Finally, we find that scratchpad is not always helpful
to solve length generalization and its format highly impacts the model's
performance. Overall, our work suggests that explicit position embeddings are
not essential for decoder-only Transformers to generalize well to longer
sequences.

本文研究了基于解码器的 Transformer 模型在用不同的位置编码方式时对长度泛化的影响，发现在一系列的推理和数学任务中，NoPE 的表现比其它方法更为优秀，而且无需额外计算。理论上，NoPE 能够代表绝对和相对位置嵌入，但在使用 SGD 训练时，它主要呈现 T5 相对位置嵌入的注意力模式。同时，scratchpad 并不总是有助于解决长度泛化问题，其格式对模型的性能有很大的影响。总之，本文的工作表明解码器 - only 的 Transformer 不一定需要显式的位置嵌入以在更长的序列上泛化良好。