Papers with NoPE
SWAN: An Efficient and Scalable Approach for Long-Context Language Modeling (2025.emnlp-main)
Copied to clipboard
Krishna C Puvvada, Faisal Ladhak, Santiago Akle Serano, Cheng-Ping Hsieh, Shantanu Acharya, Somshubra Majumdar, Fei Jia, Samuel Kriman, Simeng Sun, Dima Rekesh, Boris Ginsburg
| Challenge: | Existing decoder-only models struggle with context lengths beyond their training distribution. |
| Approach: | They propose a causal Transformer architecture that generalizes robustly to sequence lengths longer than seen during training. |
| Outcome: | The proposed decoder-only architecture can generalize robustly to longer contexts . it is more computationally efficient than the standard Transformer architecture, the authors say . |
DAPE V2: Process Attention Score as Feature Map for Length Extrapolation (2025.acl-long)
Copied to clipboard
Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li
| Challenge: | Extensive experiments demonstrate that treating attention as a feature map and applying convolution as . a processing method significantly enhances Transformer performance. |
| Approach: | They propose to use the convolution operator to mimic the processing methods in computer vision to treat attention as a feature map and apply it to neighboring attention scores across different heads. |
| Outcome: | The proposed model can be adapted to various attention-related models and achieves high performance. |
Length Generalization of Causal Transformers without Position Encoding (2024.findings-acl)
Copied to clipboard
| Challenge: | Besides Transformers without position encodings, the success of NoPE provides a new way to overcome the challenge of generalizing to longer sentences. |
| Approach: | They propose a parameter-efficient tuning for searching attention heads’ best temperature hyper-parameters, which substantially expands NoPE’s context size. |
| Outcome: | The proposed tuning significantly expands NoPE's context size, allowing it to generalize to longer sentences with state-of-the-art generalization algorithms. |