Summary of Implicit Optimization Bias Of Next-token Prediction in Linear Models, by Christos Thrampoulidis
Implicit Optimization Bias of Next-Token Prediction in Linear Models
by Christos Thrampoulidis
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 optimization properties of next-token prediction (NTP), a dominant training paradigm for modern language models. The authors frame NTP as cross-entropy minimization across distinct contexts and introduce “NTP-separability conditions” that enable reaching the data-entropy lower bound. They characterize the optimization bias of gradient descent (GD) in linear models with fixed context embeddings, showing that GD selects parameters that equate logit differences to log-odds within a specific subspace. The findings extend previous research on implicit bias and prompt further investigation into NTP’s optimization and generalization properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how computers learn language by predicting the next word in a sentence. It looks at the way a popular training method, called next-token prediction (NTP), makes decisions about which words are most likely to come next. The researchers found that this method has biases that can affect its performance, and they want to understand these biases better so they can improve language models. |
Keywords
* Artificial intelligence * Cross entropy * Generalization * Gradient descent * Optimization * Prompt * Token
