@techreport{oai:ipsj.ixsq.nii.ac.jp:02006081,
 author = {中西,亮輔 and 鈴木,結友 and 鷲見,優一郎 and 光田,英司 and 二宮,由樹 and 曽根,悠太郎 and 三輪,和久},
 issue = {26},
 month = {Dec},
 note = {In recent years, large language models (LLMs) have attracted attention for their outstanding performance in many tasks such as translation and summarization. In services deployed in society, LLMs have replaced human work, and improved productivity. This paper reports the results of applying prompt optimization methods―Automatic Prompt Engineer and Automatic Prompt Optimization―to an annotation task labeling the relationship between search queries and search goals. We found that the performance of Claude 3.7 Sonnet is close to human performance (Cohen's Kappa coefficients are above 0.6) in labeling whether the words corresponding to the conceptual hierarchical relationship are included in search queries and whether the words corresponding to the attributes are as well., In recent years, large language models (LLMs) have attracted attention for their outstanding performance in many tasks such as translation and summarization. In services deployed in society, LLMs have replaced human work, and improved productivity. This paper reports the results of applying prompt optimization methods―Automatic Prompt Engineer and Automatic Prompt Optimization―to an annotation task labeling the relationship between search queries and search goals. We found that the performance of Claude 3.7 Sonnet is close to human performance (Cohen's Kappa coefficients are above 0.6) in labeling whether the words corresponding to the conceptual hierarchical relationship are included in search queries and whether the words corresponding to the attributes are as well.},
 title = {プロンプト最適化を用いた検索クエリと検索目標の関係性アノテーション},
 year = {2025}
}