In this study we compared how well DALL-E 2 visually represented the meaning
of linguistic prompts also given to young children in comprehension tests.
Sentences representing fundamental components of grammatical knowledge were
selected from assessment tests used with several hundred English-speaking
children aged 2-7 years for whom we had collected original item-level data.
DALL-E 2 was given these prompts five times to generate 20 cartoons per item,
for 9 adult judges to score. Results revealed no conditions in which DALL-E
2-generated images that matched the semantic accuracy of children, even at the
youngest age (2 years). DALL-E 2 failed to assign the appropriate roles in
reversible forms; it failed on negation despite an easier contrastive prompt
than the children received; it often assigned the adjective to the wrong noun;
it ignored implicit agents in passives. This work points to a clear absence of
compositional sentence representations for DALL-E 2.

DALL-E 2 在语义准确性方面无法与年幼儿童相媲美，这表明它在组合句子表示方面存在明显缺陷。

DALL-E 2 中组合句法和语义的比较研究

A Comparative Investigation of Compositional Syntax and Semantics in  DALL-E 2

In this paper, we present a pipeline for image extraction from historical
documents using foundation models, and evaluate text-image prompts and their
effectiveness on humanities datasets of varying levels of complexity. The
motivation for this approach stems from the high interest of historians in
visual elements printed alongside historical texts on the one hand, and from
the relative lack of well-annotated datasets within the humanities when
compared to other domains. We propose a sequential approach that relies on
GroundDINO and Meta's Segment-Anything-Model (SAM) to retrieve a significant
portion of visual data from historical documents that can then be used for
downstream development tasks and dataset creation, as well as evaluate the
effect of different linguistic prompts on the resulting detections.

利用基础模型提取历史文献中的图像，并评估不同语言提示对人文学科不同级别的数据集上的效果。