Summing Up the Facts: Additive Mechanisms Behind Factual Recall in LLMs
Bilal Chughtai Alan Cooney Neel Nanda
How do transformer-based large language models (LLMs) store and retrieve
knowledge? We focus on the most basic form of this task -- factual recall,
where the model is tasked with explicitly surfacing stored facts in prompts of
form `Fact: The Colosseum is in the country of'. We find that the mechanistic
story behind factual recall is more complex than previously thought. It
comprises several distinct, independent, and qualitatively different mechanisms
that additively combine, constructively interfering on the correct attribute.
We term this generic phenomena the additive motif: models compute through
summing up multiple independent contributions. Each mechanism's contribution
may be insufficient alone, but summing results in constructive interfere on the
correct answer. In addition, we extend the method of direct logit attribution
to attribute an attention head's output to individual source tokens. We use
this technique to unpack what we call `mixed heads' -- which are themselves a
pair of two separate additive updates from different source tokens.