A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Task
This provides insights into transformer operating principles for AI researchers, though it is incremental as it builds on existing behavioral studies without direct application to real-world problems.
The paper tackled the problem of understanding the internal mechanisms of transformers in reasoning tasks by conducting a mechanistic analysis on a synthetic symbolic multi-step reasoning task, identifying interpretable mechanisms like depth-bounded recurrence and parallel operation with intermediate storage in token positions.
Transformers demonstrate impressive performance on a range of reasoning benchmarks. To evaluate the degree to which these abilities are a result of actual reasoning, existing work has focused on developing sophisticated benchmarks for behavioral studies. However, these studies do not provide insights into the internal mechanisms driving the observed capabilities. To improve our understanding of the internal mechanisms of transformers, we present a comprehensive mechanistic analysis of a transformer trained on a synthetic reasoning task. We identify a set of interpretable mechanisms the model uses to solve the task, and validate our findings using correlational and causal evidence. Our results suggest that it implements a depth-bounded recurrent mechanisms that operates in parallel and stores intermediate results in selected token positions. We anticipate that the motifs we identified in our synthetic setting can provide valuable insights into the broader operating principles of transformers and thus provide a basis for understanding more complex models.