A Mathematical Framework for Transformer Circuits (2021)
This paper explores a mathematical framework for understanding transformer circuits, the architecture behind modern language models like GPT-3. It proposes using mechanistic interpretability to reverse-engineer these models to better identify and mitigate potential safety risks.
Why it matters
Understanding the internal mechanics of large language models is critical for ensuring their reliability and safety as they become more integrated into real-world systems.
Transformer Circuits Thread A Mathematical Framework for Transformer Circuits
Transformer language models are an emerging technology that is gaining increasingly broad real-world use, for example in systems like GPT-3 , LaMDA , Codex , Meena , Gopher , and similar models. However, as these models scale, their open-endedness and high capacity creates an increasing scope for unexpected and sometimes harmful behaviors. Even years after a large model is trained, both creators and users routinely discover model capabilities – including problematic behaviors – they were previously unaware of.
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