Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations
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 \texttt{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 c...
| Authors | Guy Rotman, Adi Kopilov, Danit Berger Zalmanson et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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