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[2508.18210] Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation
Summary
This article presents a diagnostic framework for evaluating synthetic dialogue generation in contact centers, highlighting the limitations of current methods in capturing realistic interactions.
Why It Matters
As synthetic data becomes essential for contact centers due to privacy constraints, understanding its limitations is crucial for improving dialogue generation technologies. This research provides a structured approach to evaluate and enhance the realism of synthetic dialogues, which is vital for effective customer interactions.
Key Takeaways
- Current synthetic dialogue generation methods struggle to replicate realistic agent-customer interactions.
- A new diagnostic evaluation framework with 17 metrics assesses the quality of synthetic dialogues.
- Synthetic transcripts often lack fidelity in sentiment and conversational realism compared to real dialogues.
- Structured supervision does not fully bridge the gap in generating realistic conversations.
- Improving synthetic dialogue generation is essential for enhancing customer service applications.
Computer Science > Computation and Language arXiv:2508.18210 (cs) [Submitted on 25 Aug 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation Authors:Rishikesh Devanathan, Varun Nathan, Ayush Kumar View a PDF of the paper titled Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation, by Rishikesh Devanathan and 2 other authors View PDF HTML (experimental) Abstract:Synthetic data is increasingly critical for contact centers, where privacy constraints and data scarcity limit the availability of real conversations. However, generating synthetic dialogues that are realistic and useful for downstream applications remains challenging. In this work, we benchmark multiple generation strategies guided by structured supervision on call attributes (Intent Summaries, Topic Flows, and Quality Assurance (QA) Forms) across multiple languages. To test downstream utility, we evaluate synthetic transcripts on an automated quality assurance (AutoQA) task, finding that prompts optimized on real transcripts consistently outperform those optimized on synthetic transcripts. These results suggest that current synthetic transcripts fall short in capturing the full realism of real agent-customer interactions. To highlight these downstream gaps, we introduce a diagnostic evaluation framework comprising 17 metrics across four dimensions: (1) Emotional and Sentiment A...