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
The increasing complexity and length of real-world B2B conversations necessitates more efficient and effective in-context learning techniques for AI, prompting research into distillation methods.
This research addresses a critical limitation in current in-context learning, improving the practical applicability of AI for complex, specialized domains like B2B sales and potentially accelerating automation of white-collar tasks.
A new method for distilling examples into task instructions enhances AI's ability to classify and understand intricate B2B conversations, moving beyond previous limitations caused by context length and semantic complexity.
- · AI developers
- · SaaS providers
- · Sales organizations
- · Customer service platforms
- · Manual data classifiers
- · Legacy AI models with poor ICL
Improved accuracy and efficiency of AI classification for B2B conversations.
Accelerated development and adoption of AI-driven sales and support automation reducing operational costs.
Enhanced AI agents capable of more sophisticated interactions, potentially displacing certain human roles in sales and business intelligence.
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Read at arXiv cs.CL