The Inside View
Eric Michaud on scaling, grokking and quantum interpretability
Wed Jul 12 2023Description
This episode explores neural networks and their scaling, deep neural networks and knowledge clusters, power law distribution in language models, grammar rules and scaling exponents, grokking and generalization in neural networks, and mechanisms and interpretability in neural networks.
Insights
Neural Networks and Scaling
Understanding the internals of AI models can help avoid worst-case alignment failures. A better understanding of neural networks could be useful for alignment and avoiding risks associated with advanced AI systems.
Deep Neural Networks and Knowledge
Deep neural networks can be seen as collections of circuits that perform specialized computations. The quantization model of neural scaling explores the idea that there are specific pieces of knowledge or computational abilities called 'quanta' that are necessary for good prediction performance.
Knowledge Clusters in Language Models
The clusters reveal interesting patterns and commonalities in the knowledge required for prediction. Understanding these quanta could help understand the network's performance and what it has learned.
Power Law Distribution and Cluster Sizes
Smaller models learn more frequently useful pieces of knowledge, while larger models learn more niche knowledge. Empirical evidence suggests that cluster sizes drop off as a power law, but it is a tentative result.
Grammar Rules and Scaling Exponents
The relationship between data scaling exponent and parameter scaling exponent in neural networks is ambiguous. Overall, the scaling exponents in neural scaling are messier than expected.
Grokking and Generalization in Neural Networks
Grokking involves both a memorization phase and a slower process of forming circuits that perform structured computations related to the desired operation. Understanding grokking in neural networks is exciting because it reveals surprising and potentially valuable insights into their behavior.
Mechanisms and Interpretability in Neural Networks
Decomposing the behavior of large language models into multiple mechanisms could enable interpretability and other applications like mechanistic anomaly detection. Identifying specific mechanisms used by the model for inference could help understand its reasoning process.
Chapters
- Neural Networks and Scaling
- Deep Neural Networks and Knowledge
- Knowledge Clusters in Language Models
- Power Law Distribution and Cluster Sizes
- Grammar Rules and Scaling Exponents
- Grokking and Generalization in Neural Networks
- Mechanisms and Interpretability in Neural Networks
Neural Networks and Scaling
00:00 - 07:35
- The Quantization Model of Neural Scaling paper explores the difference between small and large networks in terms of what they learn.
- Understanding the internals of AI models can help avoid worst-case alignment failures.
- A better understanding of neural networks could be useful for alignment and avoiding risks associated with advanced AI systems.
- The engineering of neural networks is ahead of the science, similar to how steam engines were developed before a modern understanding of thermodynamics.
- Eric's background includes working on radio astronomy and deep learning in neuroscience.
- There is ongoing research on interpretability within neural networks, exploring combinations of neurons and superposition as possible explanations for their behavior.
Deep Neural Networks and Knowledge
07:10 - 15:06
- Neurons in deep neural networks can be more messy and respond to multiple things due to superposition.
- The level of abstraction in understanding deep neural networks could be similar to the brain, where looking at groups of neurons or circuits might provide more meaningful information.
- Deep neural networks can be seen as collections of circuits that perform specialized computations.
- Discreteness in deep neural networks can emerge through phase transitions during training, where certain capabilities become relevantly present.
- Smooth loss curves in deep neural networks can average over small discrete phase changes in network performance.
- The quantization model of neural scaling explores the idea that there are specific pieces of knowledge or computational abilities called 'quanta' that are necessary for good prediction performance.
- The frequency at which these quanta are useful for prediction may follow a power law distribution.
Knowledge Clusters in Language Models
14:37 - 22:03
- Language models need to know specific knowledge in order to predict well in natural language tasks.
- The paper clusters samples based on the similarity of the model's gradients for those samples.
- The clusters reveal interesting patterns and commonalities in the knowledge required for prediction.
- The clustering approach used is spectral clustering, which computes cosine similarity between gradient vectors.
- Clusters include predicting new lines at the end of text, requiring knowledge of line lengths.
- These clusters are evidence of quanta or common pieces of knowledge needed for prediction.
- Understanding these quanta could help understand the network's performance and what it has learned.
- The experiments were conducted on Pythia models ranging from 70 million to 12 billion parameters.
- Scaling curves of loss values can reveal different behaviors and patterns for individual tokens.
- Some tokens involve facts and have sharp scaling curves, while others have smooth curves with no specific piece of knowledge.
Power Law Distribution and Cluster Sizes
21:36 - 28:54
- The model's loss drops to approximately zero at a given scale, indicating the involvement of facts.
- There may be tokens that involve facts and tokens that are based on intuition or heuristics.
- The dataset used for training has over a hundred million tokens.
- The clustering was done automatically, resulting in both low-quality and high-quality clusters.
- The clustering exercise could be useful for mechanistic interpretability.
- The hypothesis is that there is a power law governing the usefulness of knowledge for prediction.
- Smaller models learn more frequently useful pieces of knowledge, while larger models learn more niche knowledge.
- Empirical evidence suggests that cluster sizes drop off as a power law, but it is a tentative result.
Grammar Rules and Scaling Exponents
28:37 - 36:20
- Clusters of grammar rules can be found in language models, such as the rule for predicting 'T' after 'DoN apostrophe' for 'don't'.
- Comma is often placed after the word 'however', which is another cluster.
- Even small language models possess this knowledge.
- The structure of language data and the problem of predicting language are still open questions.
- The relationship between data scaling exponent and parameter scaling exponent in neural networks is ambiguous.
- Different studies have shown varying results, with some fitting our theory and others not.
- Overall, the scaling exponents in neural scaling are messier than expected.
- The scaling exponents refer to the slope of the power law.
Grokking and Generalization in Neural Networks
36:04 - 43:48
- Neural networks can generalize long after they first overfit their training data, a phenomenon known as grokking.
- Grokking was discovered by researchers at OpenAI who trained small transform models to learn basic operations and observed that the networks would initially memorize the training data but eventually generalize to unseen examples with further training.
- Grokking is different from double descent, but there may be a connection between the two.
- The network's ability to fit the training data may involve different mechanisms that are learned at different rates and incentivized in different amounts over the training process.
- Understanding grokking in neural networks is exciting because it reveals surprising and potentially valuable insights into their behavior.
- In one paper on grokking, researchers explored how generalization in neural networks depends on structured representations of inputs, such as arranging embedding vectors for modular addition on a circle.
- Grokking involves both a memorization phase and a slower process of forming circuits that perform structured computations related to the desired operation.
- Neil and Matt discovered that the network's internal algorithm for modular addition explains the observed ring-shaped structure of embeddings.
- The paper on representation learning in grokking is titled 'Towards Understanding Grokking: An Effective Theory of Representation Learning' and was presented at NURIPS 2022.
Mechanisms and Interpretability in Neural Networks
43:21 - 48:08
- The paper on representation learning was called Towards Understanding Grokking and Effective Theory of Representation Learning.
- Omni-Grock is a paper that shows delayed generalization on tasks beyond algorithmic data.
- In Omni-Grock, the network was trained on fewer samples and initialized with big weights, leading to memorization and eventual generalization.
- This research aims to identify mechanisms in models responsible for certain behaviors and generalization.
- Decomposing the behavior of large language models into multiple mechanisms could enable interpretability and other applications like mechanistic anomaly detection.
- Identifying specific mechanisms used by the model for inference could help understand its reasoning process.