將範例提煉為任務指令：真實世界B2B對話的增強情境學習

摘要

情境學習（ICL）是低資源分類的標準方法，但其在專業領域的有效性仍鮮少被探討。我們針對語義複雜的多方B2B對話分類挑戰，傳統ICL在此情境下因須串接多個少量範例而導致上下文長度增加，進而產生顯著限制。為此，我們提出Call Playbook資料集，涵蓋五項源自真實B2B對話、聚焦核心銷售概念的分類任務。為彌合效能與實用性之間的差距，我們提出創新的知識萃取方法，將冗長範例精煉為結構化分類標準及精確任務描述的緊湊可解釋表徵。相較傳統ICL，我們的方法減少99%的標記使用量，並將宏平均AUC提升高達7%。值得注意的是，當上下文增長時，本方法仍維持穩健性，反觀先進的標記壓縮基準方法則下降超過9個F1分數。更重要的是，我們的框架能直接優化分類邏輯，滿足真實世界NLP應用對透明度、效率與使用者互動的關鍵需求。

English

In-context learning (ICL) is the standard method for low-resource classification, yet its efficacy in specialized domains remains largely unexplored. We address the challenge of classifying semantically complex, multi-party B2B conversations, where traditional ICL encounters significant limitations, especially as context length increases due to the concatenation of multiple few-shot examples. We introduce the Call Playbook dataset, featuring five classification tasks derived from real-world B2B conversations targeting core sales concepts. To bridge the gap between performance and practical utility, we propose novel knowledge extraction methods that distill verbose examples into compact, interpretable representations of structured classification criteria and precise task descriptions. Our approach achieves a 99\% reduction in token usage and improves macro-averaged AUC by up to 7\% over traditional ICL. Notably, it remains robust as context grows, unlike advanced token compression baselines which degrade by over 9 F1 points. Importantly, our framework enables direct refinement of classification logic, addressing critical needs for transparency, efficiency, and user interaction in real-world NLP applications.