Biology of Large Language Models

Researchers analyzed the model's internal mechanisms across diverse tasks like multi-step reasoning, poetry generation, multilingual translation, and arithmetic. They identified interpretable "features" and mapped their interactions using "attribution graphs," offering insights into how the model performs computations. The study uncovers sophisticated strategies such as forward and backward planning, reveals the interplay of language-specific and abstract circuits, and examines phenomena like hallucination and refusal behavior. Through targeted interventions, the authors validated their hypotheses about the underlying computational processes, providing a deeper understanding of the model's "biology." Ultimately, this work aims to advance the field of AI interpretability and contribute to safer, more transparent large language models.

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