Summary of Entp: Encoder-only Next Token Prediction, by Ethan Ewer et al.
ENTP: Encoder-only Next Token Prediction
by Ethan Ewer, Daewon Chae, Thomas Zeng, Jinkyu Kim, Kangwook Lee
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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 A novel approach to next-token prediction is introduced, departing from conventional methods that rely on decoder-only Transformers with causal attention. The proposed Encoder-only Next Token Prediction (ENTP) model is explored for its expressive power and complexity compared to traditional approaches. Notably, ENTP is shown to excel in settings where compute resources are unlimited, outperforming decoder-only Transformers in tasks such as addition, in-context learning, and language modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Next-token prediction uses a new way of thinking about text understanding. Instead of using the usual method with transformers, researchers have come up with a different approach called ENTP. This lets them do more complex tasks and work better when they have lots of computer power. They tested this on some simple math problems, learning in context, and language modeling, and it did really well. |
Keywords
» Artificial intelligence » Attention » Decoder » Encoder » Token
