Summary of Hierarchical Skip Decoding For Efficient Autoregressive Text Generation, by Yunqi Zhu et al.
Hierarchical Skip Decoding for Efficient Autoregressive Text Generation
by Yunqi Zhu, Xuebing Yang, Yuanyuan Wu, Wensheng Zhang
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed Hierarchical Skip Decoding (HSD) method is a plug-and-play approach that efficiently generates texts using pre-trained language models while maintaining high-quality outputs. By adaptively skipping decoding layers based on sequence length, HSD reduces computational workload and allocates resources effectively. Compared to vanilla autoregressive decoding, HSD achieves 90% of the text quality with almost half of the layers skipped, outperforming competitive approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to generate texts using pre-trained language models. The method is called Hierarchical Skip Decoding (HSD). It’s like a shortcut that helps computers work faster without losing the quality of what they’re writing. HSD looks at how long the text needs to be and decides which parts don’t need as much detail, so it can skip over those parts. This makes it faster and more efficient. The paper tests HSD on five different datasets and shows that it works really well, even when it’s using half as many layers as usual. |
Keywords
» Artificial intelligence » Autoregressive
