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About this paper
Varsha Embar, Ritvik Shrivastava, Vinay Damodaran, Travis Mehlinger, Yu-Chung Hsiao, Karthik Raghunathan
A system extracts concise "call drivers" from customer call transcripts using fine-tuned open-weight LLMs, enabling precise topic modeling and offering substantial cost reductions for enterprise deployment compared to proprietary models.
Key insights
- Call drivers provide a more precise, actionable, and less ambiguous alternative to traditional topic modeling, serving as a robust foundation for downstream analytics.
- Fine-tuning open-weight LLMs can achieve high-quality call driver generation and superior topic modeling results while being cost-effective compared to proprietary models.
- Strategic infrastructure design and optimization techniques (e.g., input compression, 4-bit quantization, spot instances) are critical for scalable and affordable LLM deployment in enterprise contact centers.
- The "Call Driver Score" effectively evaluates model performance without requiring full end-to-end pipeline evaluation for topic modeling systems
Summary by alphaXiv.
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