A third kind of gap is not about missing text at all. It is about text that sits too close together.
Suppose the training text contains many sentences about two similar things — two researchers with the same last name, two companies in the same industry, two cities with similar names, two versions of the same product. The model does not store these as separate records with labels. It stores patterns of which words tend to appear near which other words. When two topics share a lot of surrounding vocabulary, their patterns overlap.
Now ask a question that points at one of them. The model pulls from the overlapping region, where pieces of both topics are mixed. The answer can contain a real name from one topic attached to a real detail from the other. Every piece is genuine; the combination is not. This is why blended answers are so hard to catch — nothing in them is obviously invented, so there is no odd word to notice. The error is in the join, not in the parts.
The same overlap explains why an unfamiliar question is riskier than a familiar one. A familiar question sits in a dense, well-separated region of patterns, so the model has plenty of consistent material to draw on. An unfamiliar question sits near the edges, where the model has less to work with and the nearest patterns may belong to a topic that only looks similar. The model has no way to tell the difference between 'this is the topic' and 'this is the closest topic I have'.