Summary of When Can Transformers Count to N?, by Gilad Yehudai et al.
When Can Transformers Count to n?
by Gilad Yehudai, Haim Kaplan, Asma Ghandeharioun, Mor Geva, Amir Globerson
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Medium Difficulty summary: This paper investigates whether large language models based on transformer architectures can solve very simple counting tasks, such as counting the frequency of a token in a string. The authors show that if the dimension of the transformer state is linearly related to the context length, this task can be solved. However, they propose a solution that does not scale beyond this limit and provide theoretical arguments for why it is unlikely for a size-limited transformer to implement this task. Empirical results demonstrate a phase transition in performance, as anticipated by the theoretical argument. This work highlights the importance of understanding how transformers can solve simple tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers studied whether very powerful language models can do something as simple as count how many times a word appears in a sentence. They found that if these models have enough “brain power” to handle long sentences, they can count correctly. However, the authors also showed that if the model is limited in size, it cannot solve this task no matter how long the sentence is. The results show that there are certain limits to what even very powerful language models can do. |
Keywords
* Artificial intelligence * Context length * Token * Transformer
