Summary of Next-token Prediction Capacity: General Upper Bounds and a Lower Bound For Transformers, by Liam Madden et al.
Next-token prediction capacity: general upper bounds and a lower bound for transformers
by Liam Madden, Curtis Fox, Christos Thrampoulidis
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 paper investigates the capabilities of decoder-only transformers in predicting next-token probability distributions. The authors establish upper and lower bounds on the number of distinct context sequences these models can interpolate, which are equal up to a multiplicative constant. They prove these bounds for both arbitrary and finite document sequence settings, highlighting an important injectivity property satisfied by self-attention. Additionally, they provide numerical evidence that the minimal number of parameters for memorization is sufficient for training the model to its entropy lower bound. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well machines can predict what comes next in a series of words or tokens. It finds out how many different things these machines can understand and predict based on previous information. The results show that there’s an important connection between self-attention, which helps machines learn about relationships, and the number of unique sequences they can work with. |
Keywords
» Artificial intelligence » Decoder » Probability » Self attention » Token
