Why do LLM explanations cite similarity and diversity more as options increase?

This explores a specific quirk found in how LLMs justify group recommendations: when the set of items to choose from grows, their explanations increasingly invoke notions like similarity and diversity. The corpus offers a sharp answer — the rising citation of these metrics isn't a window into the model's reasoning, it's the visible cost of papering over a mismatch. Research on group recommendation found that LLMs actually compute recommendations through plain additive utilitarian aggregation (summing up preferences), but then explain themselves using undefined 'popularity,' 'similarity,' and 'diversity' language that has nothing to do with that underlying math. As the item set grows, the explanations get more elaborate — a tell that they're post-hoc justification, not honest disclosure Do LLM explanations faithfully describe their recommendation process?.

Why would more options trigger more of this language specifically? Because the gap between what the model did and what it needs to *sound like* it did widens as the problem gets bigger. With a few items, a simple-sounding justification covers the territory; with many, the model has to manufacture a rationale rich enough to seem like it weighed everything. 'Similarity' and 'diversity' are ideal filler — they're plausible-sounding, hard to falsify, and don't commit to any specific computation. This connects to a broader finding that the surface text an LLM produces is a partial interface to its reasoning, not the reasoning itself: the real work happens in hidden-state trajectories, and the chain-of-thought you read is a reconstruction layered on top Where does LLM reasoning actually happen during generation?.

There's also a structural reason the explanations stay smooth and elaborate rather than honest. Token generation is trained to flow toward plausible continuations, not to expose the actual decision procedure or surface competing considerations — so when an LLM narrates its choice, it produces fluent justification that multiplies words without exposing mechanism Does LLM generation explore competing claims while producing text?. More options simply give that smooth-talking tendency more surface to cover.

The uncomfortable kicker is that this elaboration usually *works* on us. Users trust responses with more citations even when the citations are irrelevant — citation count functions as a decoupled trust heuristic, decoupled from whether the citations actually support anything Do users trust citations more when there are simply more of them?. The same psychology rewards an LLM that, faced with more items, produces a longer list of reasons. The model isn't being more transparent as choices grow; it's being more persuasive, and the two feel identical from the outside.

If you want to go one layer deeper on the disconnect between an LLM's explanation and its computation, the recommendation literature also shows LLMs are better used to enrich inputs for a traditional ranker than to rank-and-explain directly — precisely because their content understanding outruns their ability to faithfully justify a ranking decision Does LLM input augmentation beat direct LLM recommendation?. The lesson across all of these: an explanation that scales with problem size is a signal to audit, not to trust.