arXiv:2406.17241v1 Announce Type: new
Abstract: In the field of language model interpretability, circuit discovery is gaining popularity. Despite this, the true meaning of these circuits remain largely unanswered. We introduce a novel method to learn their meanings as a holistic object through the lens of knowledge editing. We extract circuits in the GPT2-XL model using diverse text classification datasets, and use hierarchical relations datasets to explore knowledge editing in the circuits. Our findings indicate that these circuits contain entity knowledge but resist new knowledge more than complementary circuits during knowledge editing. Additionally, we examine the impact of circuit size, discovering that an ideal “theoretical circuit” where essential knowledge is concentrated likely incorporates more than 5% but less than 50% of the model’s parameters. We also assess the overlap between circuits from different datasets, finding moderate similarities. What constitutes these circuits, then? We find that up to 60% of the circuits consist of layer normalization modules rather than attention or MLP modules, adding evidence to the ongoing debates regarding knowledge localization. In summary, our findings offer new insights into the functions of the circuits, and introduce research directions for further interpretability and safety research of language models.
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