5 papers · 1 filter
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…
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…
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…
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity
Sho Hoshino, Akihiko Kato, Soichiro Murakami +1
Learning better sentence embeddings leads to improved performance for natural language understanding tasks including semantic textual similarity (STS) and natural language inferenc…
NTT's Machine Translation Systems for WMT19 Robustness Task
Soichiro Murakami, Makoto Morishita, Tsutomu Hirao +1
This paper describes NTT's submission to the WMT19 robustness task. This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulti…