Do larger models follow stated beliefs less often?

The paper introduces EoBench to test how a user's "expression of belief" (EoB) changes whether a model follows the context it was given or falls back on prior knowledge. Across 18 LLMs spanning Llama3, Qwen3 and Gemma3, at 1B to 30B parameters, in base and instruct variants, the abstract reports that "bigger models and instruction-tuned models tend to be less context-following than smaller models and base models." The discussion restates both trends: instruction-tuning reduces the context-following rate, and so does increasing model size. The framing is deliberately two-sided. In some situations the ideal is for the model to accept the user's stated belief as true, and in others it is to stick to what it knows.

The benchmark is built to isolate phrasing. It pairs EoBs with world-knowledge facts to produce controlled EoB–query pairs, using a typology of four linguistically motivated dimensions (form, evidentiality, epistemic stance, tone) spanning 19 fine-grained types. The introduction's example is the same false claim delivered two ways: stated outright, "The Eiffel Tower is located in Berlin," or presupposed through a question, "When was the Eiffel Tower relocated to Berlin?" On the paper's explanation, instruction-tuning "directly incentivizes models to attend more strongly to in-context instructions or demonstrations." For size the authors say the cause "is less clear" and speculate that larger parameter counts "provide more storage for parametric knowledge and thus stronger priors."

The size result fits Do large language models reason symbolically or semantically?, where counter-commonsense rules in context lose to parametric commonsense. EoBench adds a scale and training-stage axis to that picture of prior knowledge beating context. It sits close to Why do language models fact-check instead of confirming beliefs?, where models asked to acknowledge a belief default to fact-checking the embedded claim. Both describe models that lean on what they know over what the user said, and the aggregate trend here is compatible with that account. The two papers measure different things, though. One tests belief acknowledgment across verbs, and the other tests context versus prior knowledge across expression types, so this note does not claim they show the same mechanism.

The excerpt leaves most of the title's promise open. It does not say which of the 19 types are more or less persuasive, or whether form, evidentiality, stance and tone interact with the size and training-stage trends. It gives no effect sizes, no per-family breakdown, no description of the fact set and no metric definition, and "tend to" signals a trend rather than a rule. The instruction-tuning explanation is also stated without saying why stronger attention to instructions would lower following of a stated belief. The size explanation is labeled speculation, and the authors point to causal abstraction and mechanistic interpretability as the route to a real account. What the excerpt does support is narrow but usable: a bigger or more heavily instruction-tuned model is not automatically the more accommodating one, and whether lower context-following counts as a failure depends on whether the user's belief should have been accepted.