2 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
Training-free Conditional Image Embedding Framework Leveraging Large Vision Language Models
Masayuki Kawarada, Kosuke Yamada, Antonio Tejero-de-Pablos +1
Conditional image embeddings are feature representations that focus on specific aspects of an image indicated by a given textual condition (e.g., color, genre), which has been a ch…
Do LLMs and Humans Find the Same Questions Difficult? A Case Study on Japanese Quiz Answering
Naoya Sugiura, Kosuke Yamada, Yasuhiro Ogawa +2
LLMs have achieved performance that surpasses humans in many NLP tasks. However, it remains unclear whether problems that are difficult for humans are also difficult for LLMs. This…
FrameEOL: Semantic Frame Induction using Causal Language Models
Chihiro Yano, Kosuke Yamada, Hayato Tsukagoshi +2
Semantic frame induction is the task of clustering frame-evoking words according to the semantic frames they evoke. In recent years, leveraging embeddings of frame-evoking words th…
Out-of-the-Box Conditional Text Embeddings from Large Language Models
Kosuke Yamada, Peinan Zhang
Conditional text embedding is a proposed representation that captures the shift in perspective on texts when conditioned on a specific aspect. Previous methods have relied on exten…