Deep Papers

LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic

Jun 14, 2024

Delve into recent research on LLM interpretability with k-sparse autoencoders from OpenAI and sparse autoencoder scaling laws from Anthropic. Explore the implications for understanding neural activity and extracting interpretable features from language models.

44:00

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Quick takeaways

Sparse autoencoders improve interpretability in LLMs, simplifying feature extraction and tuning.

Scaling laws can guide training of sparse autoencoders to extract interpretable features from language models.

Deep dives

Features in Sparse Autoencoders for Model Interpretability

Researchers discussed using sparse autoencoders to map features, striving for interpretability within models. They focused on understanding what happens within layers of neural networks, aiming to comprehend the hidden dimensions and activations. The challenge was to grasp the inner workings of the network, accentuating the imbalance between high-level structural knowledge and specifics on network functionality.

Intro

5min

Interpretability in LLMs: Feature Research Perspective

28min

Exploring the Importance of Intermediary Steps and Features in Machine Learning Models

2min

Exploration of Searching for Features and Ensuring Model Safety

5min

Exploration of Model Interpretability and Future Research Directions

2min

Efficiency through Feature Activation and Model Optimization

2min

It’s been an exciting couple weeks for GenAI! Join us as we discuss the latest research from OpenAI and Anthropic. We’re excited to chat about this significant step forward in understanding how LLMs work and the implications it has for deeper understanding of the neural activity of language models. We take a closer look at some recent research from both OpenAI and Anthropic. These two recent papers both focus on the sparse autoencoder--an unsupervised approach for extracting interpretable features from an LLM. In "Extracting Concepts from GPT-4," OpenAI researchers propose using k-sparse autoencoders to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. In "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet," researchers at Anthropic show that scaling laws can be used to guide the training of sparse autoencoders, among other findings.

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Deep Papers

LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Features in Sparse Autoencoders for Model Interpretability

Feature Activation and Behavior Implications

Mapping Features for Model Understanding

Ensuring Model Safety and Ethical Considerations

Remember Everything You Learn from Podcasts