We develop an embedding model specifically designed for Waka poetry and use it to build a model for detecting Honkadori. Waka is a tradi-tional form of old Japanese poetry that has been composed since ancient times. Honkadori is a sophisticated poetic technique in Japanese clas-sical literature where poets incorporate words or poetic sentiments from old Wakas (Honka) into their own work. First, we fine-tune a pre-trained language model using contrastive learn-ing to construct a Waka-specialized embedding model. Then, using the embedding vectors ob-tained from this model and features extracted from them, we train a machine learning model to detect the Honka (original poem) of Wakas that employ the Honkadori technique. Using paired data of Honka and Wakas that are consid-ered to use Honkadori, we evaluated the Honka detection model and demonstrated that it can detect Honka with reasonable accuracy.

We investigate the impact of politeness levels in prompts on the performance of large language models (LLMs). Polite language in human communications often garners more compliance and effectiveness, while rudeness can cause aversion, impacting response quality. We consider that LLMs mirror human communication traits, suggesting they align with human cultural norms. We assess the impact of politeness in prompts on LLMs across English, Chinese, and Japanese tasks. We observed that impolite prompts often result in poor performance, but overly polite language does not guarantee better outcomes. The best politeness level is different according to the language. This phenomenon suggests that LLMs not only reflect human behavior but are also influenced by language, particularly in different cultural contexts. Our findings highlight the need to factor in politeness for cross-cultural natural language processing and LLM usage.