<figure xmlns="http://www.tei-c.org/ns/1.0"> <graphic url="Pictures/7694ba765e70088f79c32bd4aab3d167.png" /> <head>Example of two images generated with the same prompt depicting a triumphal procession in ancient Rome as described in Algül (2018).</head> </figure>We know these images are challenging to evaluate and that automatic evaluations correlate poorly with human judgment (Otani et al., 2023). A first human assessment of the 600 images by 20 history students, coupled with an automatic evaluation using LLMs like GPT-4o, Gemini, or Claude Sonnet 3.5 as evaluators, shows that prompt-to-image alignment on Likert scale from “1 – The image does not match the prompt at all.” to “5 – The image completely matches the prompt.” is sometimes highly overestimated and too generous (especially by Claude) (see Figure 2).<ref xmlns="http://www.tei-c.org/ns/1.0" n="3" target="ftn3" /> Moreover, we found that the scores on the Likert scale as assessed by LLMs are usually higher for the Roman triumph than for the <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">Lupercalia</hi>, suggesting potential knowledge gaps. We aim to close this gap by fine-tuning language and vision models on relevant material, increasing historical accuracy.