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Mechanistic interpretability research explaining how GPT-2 performs natural language tasks by analyzing internal attention mechanisms, advancing understanding of model decision-making processes crucial for AI safety and alignment.
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Abstract
Research in mechanistic interpretability seeks to explain behaviors of machine learning 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, opening opportunities to scale our understanding to both larger models and more complex tasks.
Summary
This paper presents a detailed mechanistic explanation of how GPT-2 small performs the indirect object identification (IOI) task, identifying 26 attention heads organized into 7 functional classes through causal intervention techniques. The authors evaluate their explanation using faithfulness, completeness, and minimality metrics, finding support for their model while acknowledging remaining gaps. This work represents one of the largest end-to-end reverse-engineering efforts of a natural language behavior in a language model, demonstrating that mechanistic understanding of large ML models is feasible and can potentially scale to larger models and more complex tasks.
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# Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
Kevin Wang1,Alexandre Variengien1,Arthur Conmy1,Buck Shlegeris1&Jacob Steinhardt1,2
1Redwood Research
2UC Berkeley
kevin@rdwrs.com, alexandre@rdwrs.com,
arthur@rdwrs.com, buck@rdwrs.com, jsteinhardt@berkeley.edu
###### Abstract
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](https://github.com/redwoodresearch/Easy-Transformer "").
## 1 Introduction
Transformer-based language models (Vaswani et al., [2017](https://ar5iv.labs.arxiv.org/html/2211.00593#bib.bib20 ""); Brown et al., [2020](https://ar5iv.labs.arxiv.org/html/2211.00593#bib.bib3 "")) have demonstrated an impressive suite of capabilities but largely remain black boxes. Understanding these models is difficult because they employ complex non-linear interactions in densely-connected layers and operate in a high-dimensional space. Despite this, they are already deployed in high-impact settings (Zhang et al., [2022](https://ar5iv.labs.arxiv.org/html/2211.00593#bib.bib23 ""); Caldarini et al., [2022](https://ar5iv.labs.arxiv.org/html/2211.00593#bib.bib4 "")), underscoring the urgency of understanding and anticipating possible model behaviors. Some researchers have argued that interpretability is critical for the safe deployment of advanced machine learning systems (Hendrycks & Mazeika, [2022](https://ar5iv.labs.arxiv.org/html/2211.00593#bib.bib8 "")).
Work in mechanistic interpretability aims to discover, understand, and verify the algorithms that model weights implement by reverse engineering model computation into human-understandable components (Olah, [2022](https://a
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