23 karma · joined September 9, 2013
The problem is how they inject it. Their “knowledge” isn’t natural language; it’s templated Wikidata triples like "X is the capital of Y." That’s a super low-entropy, highly repetitive distribution. When you cram enough of that into a fixed token budget, you’re not really teaching the model more facts — you’re just destroying linguistic diversity and skewing the token statistics.
In real pretraining or domain adaptation scenarios, “knowledge” tends to appear in richer, more varied contexts. The practical takeaway isn’t "don’t add too much domain data," but rather "don’t overrepresent any single format or narrow syntactic pattern" The issue seems more about representation homogeneity than about factual density itself.