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Understanding Sequence Continuation in Transformers through Circuit Analysis

Overview

This repository hosts the code and resources used in our research on interpreting transformer models, specifically focusing on the GPT-2 architecture. Our work extends existing efforts in reverse engineering transformer models into human-readable circuits. We delve into the interpretability of these models by analyzing and comparing circuits involved in sequence continuation tasks, including sequences of Arabic numerals, number words, and months.

Key Findings

  • Circuit Interpretability Analysis: We successfully identified a crucial sub-circuit within GPT-2 responsible for detecting sequence members and predicting the next member in a sequence.
  • Shared Circuit Subgraphs: Our research reveals that semantically related sequences utilize shared circuit subgraphs with analogous roles.
  • Model Behavior Predictions and Error Identification: Documenting these shared computational structures aids in better predictions of model behavior, identifying potential errors, and formulating safer editing procedures.
  • Towards Robust, Aligned, and Interpretable Language Models: Our findings contribute to the broader goal of creating language models that are not only powerful but also robust, aligned with human values, and interpretable.

To get started with our project, follow these steps:

Clone the Repository:

git clone [repository URL]

Install Dependencies:

pip install -r requirements.txt

Explore the Notebooks:

Navigate to the notebooks directory and open the Colab notebooks to see detailed analyses and visualizations.

Running Experiments

After navigating to the src/iter_node_pruning folder, use this command to run node ablation experiments. Lower --num_samps if one encounters GPU out-of-memory issues. An A100 is recommended. Change the task and other input parameters to run a different experiment.

python run_node_ablation.py --model "gpt2-small" --task "numerals" --num_samps 512 --threshold 20 --one_iter

Similarly, the other files can be run by naviating to their respective sub-folder in src. These other commands include:

python run_gen_data.py --model "gpt2-small" 

python run_edge_ablation.py --model "gpt2-small" --task "numerals" --num_samps 512 --threshold 0.8

python run_attn_pats.py --model "gpt2-small" --task "numerals" --num_samps 128 

# If an issue such as `RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'`, it could be due to the GPU not being powerful enough.
python run_logit_lens.py --model "gpt2" --task "numerals" --num_samps 512

Citation

If you use this work in your research, please cite:

@misc{lan2024interpretablesequencecontinuationanalyzing,
      title={Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models}, 
      author={Michael Lan and Philip Torr and Fazl Barez},
      year={2024},
      eprint={2311.04131},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2311.04131}, 
}