Is ChatGPT an N-gram model on steroids?

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Aug 15, 2024

In this discussion, Timothy Nguyen, a DeepMind Research Scientist and MIT scholar, shares insights from his innovative research on transformers and n-gram statistics. He reveals a method to analyze transformer predictions without tapping into internal mechanisms. The conversation covers how transformers evolve during training, particularly in curriculum learning, and how to detect overfitting without traditional holdout methods. Nguyen also dives into philosophical questions about AI understanding, highlighting the complexities of interpreting neural network behavior.

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ANECDOTE

Lion, Tiger, and Bear

Timothy Nguyen uses a concrete example from the Tiny Stories dataset to illustrate how transformers predict.
He presents a scenario where the model must choose between predicting "bear" based on the full context or other animals based on a shorter context.

INSIGHT

Form and Selection

Transformers use context by selecting relevant statistics and determining the form of those statistics.
Nguyen's research suggests there's often an n-gram statistic that approximates transformer predictions.

INSIGHT

Template Matching

Dr. Nguyen created a hash table of N-gram templates to compare against transformer predictions.
His results showed 78% of the time, a good match between a template and the transformer's prediction was found.

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DeepMind Research Scientist / MIT scholar Dr. Timothy Nguyen discusses his recent paper on understanding transformers through n-gram statistics. Nguyen explains his approach to analyzing transformer behavior using a kind of "template matching" (N-grams), providing insights into how these models process and predict language.

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Key points covered include:

A method for describing transformer predictions using n-gram statistics without relying on internal mechanisms.

The discovery of a technique to detect overfitting in large language models without using holdout sets.

Observations on curriculum learning, showing how transformers progress from simpler to more complex rules during training.

Discussion of distance measures used in the analysis, particularly the variational distance.

Exploration of model sizes, training dynamics, and their impact on the results.

We also touch on philosophical aspects of describing versus explaining AI behavior, and the challenges in understanding the abstractions formed by neural networks. Nguyen concludes by discussing potential future research directions, including attempts to convert descriptions of transformer behavior into explanations of internal mechanisms.

Timothy Nguyen's earned his B.S. and Ph.D. in mathematics from Caltech and MIT, respectively. He held positions as Research Assistant Professor at the Simons Center for Geometry and Physics (2011-2014) and Visiting Assistant Professor at Michigan State University (2014-2017). During this time, his research expanded into high-energy physics, focusing on mathematical problems in quantum field theory. His work notably provided a simplified and corrected formulation of perturbative path integrals.

Since 2017, Nguyen has been working in industry, applying his expertise to machine learning. He is currently at DeepMind, where he contributes to both fundamental research and practical applications of deep learning to solve real-world problems.

Refs:

The Cartesian Cafe

https://www.youtube.com/@TimothyNguyen

Understanding Transformers via N-Gram Statistics

https://www.researchgate.net/publication/382204056_Understanding_Transformers_via_N-Gram_Statistics

TOC

00:00:00 Timothy Nguyen's background

00:02:50 Paper overview: transformers and n-gram statistics

00:04:55 Template matching and hash table approach

00:08:55 Comparing templates to transformer predictions

00:12:01 Describing vs explaining transformer behavior

00:15:36 Detecting overfitting without holdout sets

00:22:47 Curriculum learning in training

00:26:32 Distance measures in analysis

00:28:58 Model sizes and training dynamics

00:30:39 Future research directions

00:32:06 Conclusion and future topics