Researchers Decompile Transformers into Interpretable RASP Programs

Researchers have developed a new method to extract interpretable algorithms from trained Transformer models. This technique involves re-parameterizing the Transformer into a RASP program and then using causal interventions to isolate a small, sufficient sub-program. Experiments on Transformers trained for algorithmic and formal language tasks demonstrated that this method can often recover simple RASP programs from models that exhibit length-generalization, providing strong evidence that Transformers internally implement such programs. AI

IMPACT Provides a method for understanding the internal computations of Transformer models, potentially leading to more interpretable and trustworthy AI systems.

RANK_REASON The cluster contains an academic paper detailing a new method for analyzing AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Aleksandra Bakalova
• Transformer

• paper
• model release

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