Summary of Token Alignment Via Character Matching For Subword Completion, by Ben Athiwaratkun et al.
Token Alignment via Character Matching for Subword Completion
by Ben Athiwaratkun, Shiqi Wang, Mingyue Shang, Yuchen Tian, Zijian Wang, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Rob Kwiatowski, Ramesh Nallapati, Bing Xiang
First submitted to arxiv on: 13 Mar 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 token alignment method improves generative models’ performance on text completion tasks, particularly when dealing with partial tokens. By backtracking to the last complete tokens, the approach ensures accurate generation aligns with prompts, addressing tokenization artifacts that often lead to incorrect outputs. The technique demonstrates significant improvement in various scenarios, including nuanced cases like space-prefix and partial indentation, with only a minor time increase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models can sometimes struggle when given partial information. This problem is caused by the way we break down text into smaller pieces called tokens. In this paper, researchers came up with a new technique to fix this issue. They call it token alignment. It works by looking back at the last complete tokens and making sure the generated text matches what’s given in the prompt. This helps the model make better predictions, even when it’s only given part of the information. The results show that this approach can greatly improve the performance of generative models on tasks like code completion and text autocompletion. |
Keywords
» Artificial intelligence » Alignment » Prompt » Token » Tokenization
