Summary of Empowering Character-level Text Infilling by Eliminating Sub-tokens, By Houxing Ren et al.
Empowering Character-level Text Infilling by Eliminating Sub-Tokens
by Houxing Ren, Mingjie Zhan, Zhongyuan Wu, Hongsheng Li
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed method, FIM-SE, addresses character-level infilling tasks by utilizing a line-level format to avoid predicting any sub-token in inference. The approach incorporates two special tokens to signify the rest of the incomplete lines, thereby enhancing generation guidance. Traditional methods focused on training models at the token level, leading to sub-optimal performance in character-level infilling tasks during the inference stage. FIM-SE surpasses previous methods, offering a significant advantage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Infilling tasks involve filling gaps in text with missing words or characters. The new approach, called FIM-SE, is better at doing this than other methods. It works by looking at the whole line of text instead of individual words or characters. This helps it make more accurate predictions and generate better results. |
Keywords
* Artificial intelligence * Inference * Token
