3 papers
cs.CL2025
Distilling Many-Shot In-Context Learning into a Cheat Sheet
Ukyo Honda, Soichiro Murakami, Peinan Zhang
Recent advances in large language models (LLMs) enable effective in-context learning (ICL) with many-shot examples, but at the cost of high computational demand due to longer input…
cs.CL2025
Exploring the Relationship Between Diversity and Quality in Ad Text Generation
Yoichi Aoki, Soichiro Murakami, Ukyo Honda +1
In natural language generation for advertising, creating diverse and engaging ad texts is crucial for capturing a broad audience and avoiding advertising fatigue. Regardless of the…
cs.CL2025
AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts
Soichiro Murakami, Peinan Zhang, Hidetaka Kamigaito +2
Effective linguistic choices that attract potential customers play crucial roles in advertising success. This study aims to explore the linguistic features of ad texts that influen…