"Causal World Representation in the GPT Model"

GPT's attention heads secretly learn cause-and-effect relationships while predicting next tokens.

GPT models learn causal world representations during training, enabling them to understand relationships between tokens and make more accurate predictions.

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https://arxiv.org/abs/2412.07446

🤔 Original Problem:

→ It's unclear whether GPT models truly understand causal relationships or simply predict next tokens based on surface patterns.

→ Previous research hasn't explained how GPT's attention mechanism encodes world knowledge.

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🔍 Solution in this Paper:

→ The paper interprets GPT's attention mechanism as a causal structure learner.

→ Each attention matrix represents correlations between tokens induced by underlying causal relationships.

→ The researchers developed a zero-shot method to extract causal structures from attention matrices.

→ They introduced a confidence scoring metric based on conditional independence tests.

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💡 Key Insights:

→ GPT models implicitly learn distinct causal structures for each input sequence

→ Higher structural confidence scores correlate with better adherence to domain rules

→ The attention mechanism acts as a causal discovery tool without explicit training

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📊 Results:

→ 95% accuracy in generating legal Othello game moves without explicit rule training

→ Legal move generation accuracy increases monotonically with structural confidence

→ Model performance drops significantly when causal structure confidence is low