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Mechanistic Interpretability in Large Language Models

Understanding internal representations and computational mechanisms in transformer-based language models.

Mechanistic Interpretability in Large Language Models

Abstract: We investigate the internal mechanisms of large language models through circuit analysis, revealing interpretable computational structures.

Introduction

As language models grow in size and capability, understanding their internal workings becomes increasingly critical for:

  • Safety and alignment
  • Debugging and improvement
  • Building trust in AI systems

Methodology

We employ novel techniques to map computational circuits within transformer layers:

  1. Attention head analysis
  2. Residual stream decomposition
  3. Feature attribution methods

Key Findings

Our analysis reveals:

  • Specialized attention heads for different linguistic tasks
  • Hierarchical feature composition across layers
  • Emergent computational motifs in large models

Implications

These findings suggest that even very large models develop interpretable internal structures, providing hope for scalable interpretability solutions.