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。

位置编码对Transformer中长度推广的影响