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Found 3 papers, 2 papers with code
Can Language Models Explain Their Own Classification Behavior?
To explore this, we introduce a dataset, ArticulateRules, of few-shot text-based classification tasks generated by simple rules.
Summing Up the Facts: Additive Mechanisms Behind Factual Recall in LLMs
How do transformer-based large language models (LLMs) store and retrieve knowledge?
A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations
Universality is a key hypothesis in mechanistic interpretability -- that different models learn similar features and circuits when trained on similar tasks.
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