12 papers
Simile Understanding in Text-to-Image Models: An Evaluation Framework
Luecheng Wang, Shintaro Ozaki, Hidetaka Kamigaito +4
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 fr…
Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding
Riza Setiawan Soetedjo, Yusuke Sakai, Hidetaka Kamigaito +3
Improving the quality of model-generated summaries, especially factuality, the accuracy of a summary with respect to its source content, remains a challenge. While reranking could…
TextTIGER: Text-based Intelligent Generation with Entity Prompt Refinement for Text-to-Image Generation
Shintaro Ozaki, Tomoyuki Jinno, Kazuki Hayashi +6
When generating images from prompts that include specific entities, the model must retain as much entity-specific knowledge as possible. However, the number of entities is almost c…
CodeNER: Code Prompting for Named Entity Recognition
Sungwoo Han, Hyeyeon Kim, Jingun Kwon +2
Recent studies have explored various approaches for treating candidate named entity spans as both source and target sequences in named entity recognition (NER) by leveraging large…
Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs
Soichiro Murakami, Hidetaka Kamigaito, Hiroya Takamura +1
Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In this study, we model heterogeneity in hum…
Oogiri-Master: Benchmarking Humor Understanding via Oogiri
Soichiro Murakami, Hidetaka Kamigaito, Hiroya Takamura +1
Humor is a salient testbed for human-like creative thinking in large language models (LLMs). We study humor using the Japanese creative response game Oogiri, in which participants…