Mayank Sati
2024
Probing the Depths of Language Models’ Contact-Center Knowledge for Quality Assurance
Digvijay Anil Ingle
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Aashraya Sachdeva
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Surya Prakash Sahu
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Mayank Sati
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Cijo George
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Jithendra Vepa
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
Recent advancements in large Language Models (LMs) have significantly enhanced their capabilities across various domains, including natural language understanding and generation. In this paper, we investigate the application of LMs to the specialized task of contact-center Quality Assurance (QA), which involves evaluating conversations between human agents and customers. This task requires both sophisticated linguistic understanding and deep domain knowledge. We conduct a comprehensive assessment of eight LMs, revealing that larger models, such as Claude-3.5-Sonnet, exhibit superior performance in comprehending contact-center conversations. We introduce methodologies to transfer this domain-specific knowledge to smaller models by leveraging evaluation plans generated by more knowledgeable models, with optional human-in-the-loop refinement to enhance the capabilities of smaller models. Notably, our experimental results demonstrate an improvement of up to 18.95% in Macro F1 on an in-house QA dataset. Our findings emphasize the importance of evaluation plans in guiding reasoning and highlight the potential of AI-assisted tools to advance objective, consistent, and scalable agent evaluation processes in contact centers.
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