AI Safety Fundamentals: Alignment

Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small

Apr 1, 2024

Kevin Wang, a researcher in mechanistic interpretability, discusses reverse-engineering the behavior of GPT-2 small in indirect object identification. The podcast explores 26 attention heads grouped into 7 classes, the reliability of explanations, and the feasibility of understanding large ML models. It delves into attention head behaviors, model architecture, and mathematical explanations for mechanistic interpretability in language models.

24:48

Episode guests

Kevin Wang

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Quick takeaways

Mechanistic interpretability explains behaviors of ML models by analyzing internal components like attention heads.

Understanding complex behaviors in GPT-2 small through causal interventions highlights challenges and opportunities for large ML models.

Deep dives

Interpretability in Machine Learning Models

Research in Mechanistic interpretability aims to explain behaviors of machine learning models in terms of their internal components. The podcast discusses the challenge of understanding complex behaviors in large models like GPT-2 small and presents an explanation for how the model performs a task called indirect object identification. Using interpretability approaches based on causal interventions, the researchers identify 26 attention heads grouped into seven categories to explain the model's behavior.

Introduction

3min

Analyzing Indirect Object Identification in Transformer-Based Language Models

5min

Detailed Exploration of GPT-2 Small, Attention Heads, and Model Architecture

3min

Exploring Circuits and Knockouts in Computational Models

2min

Analyzing Indirect Object Identification in GPT-2-Small

12min

Research in mechanistic interpretability seeks to explain behaviors of machine learning (ML) models in terms of their internal components. However, most previous work either focuses on simple behaviors in small models or describes complicated behaviors in larger models with broad strokes. In this work, we bridge this gap by presenting an explanation for how GPT-2 small performs a natural language task called indirect object identification (IOI). Our explanation encompasses 26 attention heads grouped into 7 main classes, which we discovered using a combination of interpretability approaches relying on causal interventions. To our knowledge, this investigation is the largest end-to-end attempt at reverse-engineering a natural behavior "in the wild" in a language model. We evaluate the reliability of our explanation using three quantitative criteria–faithfulness, completeness, and minimality. Though these criteria support our explanation, they also point to remaining gaps in our understanding. Our work provides evidence that a mechanistic understanding of large ML models is feasible, pointing toward opportunities to scale our understanding to both larger models and more complex tasks. Code for all experiments is available at https://github.com/redwoodresearch/Easy-Transformer.

Source:
https://arxiv.org/pdf/2211.00593.pdf

Narrated for AI Safety Fundamentals by Perrin Walker

A podcast by BlueDot Impact.

Learn more on the AI Safety Fundamentals website.

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AI Safety Fundamentals: Alignment

Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Interpretability in Machine Learning Models

Importance of Mechanistic Understanding

Evaluating Model Explanations

Remember Everything You Learn from Podcasts