Researchers described a method called factual priming, saying language models may use related facts to help reach the correct answer to simple factual questions. The idea is based on the observation that the models’ natural reasoning traces are not written as logical proofs, but instead surface connected facts.
The work compares this behavior with the human cognition concept of spreading activation, where a specific concept can make related concepts easier to retrieve from semantic memory. The researchers hypothesize that language models show a similar self-retrieval mechanism.
They describe factual priming as a generative process in which a model produces facts that are topically related to the question, creating a contextual bridge that helps it retrieve the correct answer.
To test this, the researchers extracted only the concrete facts from the model’s reasoning traces and removed filler text, search plans, and explicit mentions of the final target answer. They then isolated the effect of those recalled facts.
According to the source text, conditioning on a short list of recalled facts recovers most of reasoning’s gains and also helps even when reasoning is OFF.
Source: research.google.
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