4 papers
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…
AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset
Soichiro Murakami, Peinan Zhang, Hidetaka Kamigaito +2
Identifying factors that make ad text attractive is essential for advertising success. This study proposes AdParaphrase v2.0, a dataset for ad text paraphrasing, containing human p…
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…
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…