Summary of Understanding Transformers Via N-gram Statistics, by Timothy Nguyen
Understanding Transformers via N-gram Statistics
by Timothy Nguyen
First submitted to arxiv on: 30 Jun 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 This paper aims to demystify the workings of transformer-based large-language models (LLMs) by examining how they depend on context in terms of simple template functions. The authors propose a novel approach that forms families of functions based on simple N-gram statistics from the training data, and study how well these rule sets approximate transformer predictions. They discover a method to detect overfitting during training without using a holdout set, quantify the progression of transformers from learning simple to complex statistical rules, and introduce a model-variance criterion governing when transformer predictions tend to be described by N-gram rules. Additionally, they show that for 79% and 68% of LLM next-token distributions on TinyStories and Wikipedia, respectively, their top-1 predictions agree with those provided by the proposed N-gram rule sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how large-language models (LLMs) work. Currently, we don’t fully know why they are so good at language tasks. The authors take a step towards explaining this by looking at simple rules that LLMs use to make predictions. They find some interesting things: one way to spot when an LLM is overfitting (getting too good at the training data), and how LLMs learn to recognize patterns in text as they train. They also discover a way to measure how well LLMs do compared to simple rules based on word patterns. Overall, this research sheds light on how LLMs work and can help us improve them. |
Keywords
» Artificial intelligence » N gram » Overfitting » Token » Transformer
