The paper evaluates whether GPT-4o, Llama-3-8B and Mistral-7B-Instruct-v0.3 reject false presuppositions about German political-party positions or incorporate them as true. Using 2024 European-election Wahl-O-Mat positions, each fact is expressed as an affirmative direct question, a negated direct question and six loaded questions using factive verbs. Every prompt is run three times. The first two authors label direct answers, while seven annotators including the authors classify loaded responses as misinformation accommodated, rejected or imprecise; the paper reports Fleiss' kappa of 0.82 and mean pairwise Cohen's kappa of 0.72. In the published tables, GPT rejects 38.1% of loaded responses, Llama 20.7% and Mistral 10.4%; GPT and Mistral accommodate 41.4% and 64.1%, while Llama mostly produces imprecise answers (48.1%). On direct questions, GPT reaches 76.5% overall accuracy, Mistral 61.6% and Llama 51.4%. Strong direct performance does not guarantee rejection of a false premise: GPT improves in the group with six correct direct answers but still fails in other groups, and Mistral frequently accommodates despite answering many affirmative questions correctly. The paper interprets higher accuracy on affirmative than negated questions as possible disagreement avoidance or face-saving, and GPT's higher rejection rate for AfD content as possible political bias. Both accounts are exploratory: negation confounds linguistic difficulty with disagreement, loaded controls with true presuppositions are not analyzed, and face-saving is neither manipulated nor measured. The election positions also postdate the models' likely knowledge cutoffs. Auditing the public FLEX Zenodo artifact reveals a material discrepancy: the paper reports 882 loaded and 147+147 direct prompts, but the current CSVs contain 855 loaded and 184+184 direct prompts. Table 3 reproduces exactly only when including 37 prompts per condition about GRÜNE, a party omitted from the stated method; loaded tables do not reproduce because 27 prompts are missing. The file also contains invalid Y/Z labels, one empty label and one empty response. The artifact therefore supports the broad pattern but not the declared sample or every exact figure.
Research question
Do LLMs know verified political positions and, when a loaded question presupposes their negation, do they actively correct the false premise; how does that behavior vary with direct accuracy, question polarity, and party?