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This paper benchmarks topic matching methods on real-world ASR transcripts from contact centers, finding that lightweight LLM matchers with natural language descriptions outperform regex and embedding approaches.
Questions the real-world effectiveness of AI copilots for contact center agents, wondering if they truly improve speed and trust or just add distractions.