arXiv:2510. 04120v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor processing.
By Fengying Ye, Shanshan Wang, Lidia S. Chao, Derek F. Wong
The paper introduces VMetaphor-Bench, a benchmark for evaluating visual metaphor generation in text-to-image models, comprising 1,500 curated metaphors across three levels and ten categories, each paired with two prompts of varying specificity. It proposes a hybrid evaluation framework using a multiple-choice question protocol and dimension-based scoring to assess metaphorical fidelity. Experiments on 11 T2I models show that even top proprietary models struggle with compositional structuring and cross-domain mapping, underscoring the need for further research in this area.
By Chuer Chen, Zichen Wang, Yi He, Zhengxi Yu, Nan Cao
The paper introduces VMetaphor-Bench, a benchmark for assessing visual metaphor generation in text-to-image models. It contains 1,500 curated metaphors across three levels and ten categories, each paired with two prompts of varying specificity. The authors evaluate 11 T2I models using a hybrid MLLM-as-judge framework that combines a large multiple-choice question set with dimension-based scoring, finding that even top proprietary models struggle with compositional structuring and cross-domain mapping.
arXiv:2601. 03388v3 Announce Type: replace-cross Abstract: Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large language models (LLMs)' reasoning pathways, given that their training data contain a large number of metaphors.
By Zhibo Hu, Chen Wang, Yanfeng Shu, Hye-young Paik, Liming Zhu
MetaHOPE is an error‑severity‑aware annotation framework designed to evaluate how well machine translation (MT) and large language models (LLMs) translate metaphors. The authors applied MetaHOPE to three state‑of‑the‑art systems—GoogleMT, GPT5.4, and Hunyuan‑7b—using two human‑annotated metaphor corpora (VUAMC and PSUCMC) for English‑to‑Chinese and Chinese‑to‑English translation. They also produced a bilingual post‑edited gold reference, creating a new resource for metaphor translation research.
By Jiahui Liang, Lifeng Han
Similes provide a compact and expressive way to describe visual characteristics in text prompts. Recent text-to-image models (t2i models) can produce visually compelling outputs from simile prompts, yet even frontier models frequently misinterpret the metaphorical vehicle and confuse it with the object.