Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations

arXiv:2606.15641v1 Announce Type: new Abstract: 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

Source: arXiv cs.CL — read the full report at the original publisher.

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