Summary of Fourier Position Embedding: Enhancing Attention’s Periodic Extension For Length Generalization, by Ermo Hua et al.
Fourier Position Embedding: Enhancing Attention’s Periodic Extension for Length Generalization
by Ermo Hua, Che Jiang, Xingtai Lv, Kaiyan Zhang, Ning Ding, Youbang Sun, Biqing Qi, Yuchen Fan, Xuekai Zhu, Bowen Zhou
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the limitations of Rotary Position Embedding (RoPE) in extending the context length of Language Models, specifically addressing its impact on attention mechanisms. Researchers analyze the effects of RoPE across nearly all parts of LMs, uncovering adverse effects on length generalization. By applying Discrete Signal Processing theory, they demonstrate that RoPE enables periodic attention through Non-Uniform Discrete Fourier Transform. However, this periodicity is compromised by linear layers and activation functions outside of attention, as well as insufficiently trained frequency components due to time-domain truncation. To address these issues, the authors propose Fourier Position Embedding (FoPE), which enhances attention’s frequency-domain properties and improves both periodic extension and length generalization. FoPE constructs Fourier Series and zero-outs destructive frequency components, increasing model robustness against spectral damage. Experimental results across various model scales show that FoPE maintains a more stable perplexity and accuracy in a needle-in-haystack task compared to RoPE and ALiBi. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to make Language Models better at understanding longer pieces of text. It shows how the current way of doing this, called Rotary Position Embedding (RoPE), can actually make things worse when trying to understand very long texts. The researchers use special math techniques to figure out what’s going on and come up with a new approach called Fourier Position Embedding (FoPE). This new method helps the model be more consistent and accurate when understanding longer pieces of text. |
Keywords
» Artificial intelligence » Attention » Context length » Embedding » Generalization » Perplexity » Signal processing
