3 papers
cs.CL2026
When Is 0.1% Enough? Analyzing the Combined Effects of Dimensionality Reduction and Quantization on Text Embedding Compression
Riku Kisako, Hayato Tsukagoshi, Ryohei Sasano
Recent high-performing text embedding models often output high-dimensional real-valued vectors, resulting in substantial storage and computational costs. To address this issue, com…
cs.CL2025
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…
cs.CL2025
Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings
Hayato Tsukagoshi, Ryohei Sasano
Prompt-based text embedding models, which generate task-specific embeddings upon receiving tailored prompts, have recently demonstrated remarkable performance. However, their resul…