@inproceedings{
zheng2024dape,
title={{DAPE}: Data-Adaptive Positional Encoding for Length Extrapolation},
author={Chuanyang Zheng and Yihang Gao and Han Shi and Minbin Huang and Jingyao Li and Jing Xiong and Xiaozhe Ren and Michael Ng and Xin Jiang and Zhenguo Li and Yu Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=rnUEUbRxVu}
}
Positional encoding plays a crucial role in transformers, significantly impact- ing model performance and length generalization. Prior research has introduced absolute positional encoding (APE) and relative positional encoding (RPE) to distinguish token positions in given sequences. However, both APE and RPE remain fixed after model training regardless of input data, limiting their adaptability and flexibility. Hence, we expect that the desired positional encoding should be data-adaptive and can be dynamically adjusted with the given attention. In this paper, we propose a Data-Adaptive Positional Encoding (DAPE) method, which dynamically and semantically adjusts based on input context and learned fixed priors. Experimental validation on real-world datasets (Arxiv, Books3, and CHE) demonstrates that DAPE enhances model performances in terms of trained length and length generalization, where the improvements are statistically significant. The model visualization suggests that our model can keep both local and anti-local information. Finally, we successfully train the model on sequence length 128 and achieve better performance at evaluation sequence length 8192, compared with other static positional encoding methods, revealing the benefit of the adaptive positional encoding method.
DAPE: Data-Adaptive Positional Encoding for Length Extrapolation
Chuanyang Zheng Yihang Gao Han Shi Minbin Huang Jingyao Li Jing Xiong Xiaozhe Ren Michael Ng Xin Jiang Zhenguo Li Yu Li