{"slug": "operationalising-multi-dimensional-evaluation-for-conversational-agents-a-with", "title": "Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking", "summary": "Researchers introduced GenAI Evaluation, a governed pipeline for evaluating retail conversational agents at scale, processing 50,000 records daily and over two million interactions. The system achieved a macro F1 score of 0.93 and 89% human-acceptability accuracy for translation, addressing challenges in governance, reproducibility, and cost for production deployment.", "body_md": "arXiv:2607.12085v1 Announce Type: new\nAbstract: Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality. Although LLM-as-a-judge methods provide scalable alternatives to human evaluation, production deployment introduces challenges in governance, reproducibility, cost, schema consistency, traceability, and reliability. We present GenAI Evaluation, a governed, configuration-driven pipeline for large-scale evaluation of retail conversational systems. It processes production chatbot logs through normalization, sharding, asynchronous execution, and schema-constrained LLM scoring. The framework evaluates helpfulness, truthfulness, clarity, tone alignment, and translation-specific dimensions. Selective re-evaluation processes only incomplete, malformed, or schema-invalid records, while schema locking, versioned configurations, validation logs, and record-level provenance support auditability. The framework processes approximately 50,000 records daily and has evaluated more than two million interactions. Validation used 12,980 stratified-random human-labeled records from four trained annotators. Classification covered 14 intents, 156 sub-intents, 18 major domains, and 129 sub-domains. The pipeline achieved a macro F1 score of 0.93 and 89% human-acceptability accuracy for translation.", "url": "https://wpnews.pro/news/operationalising-multi-dimensional-evaluation-for-conversational-agents-a-with", "canonical_source": "https://arxiv.org/abs/2607.12085", "published_at": "2026-07-15 04:00:00+00:00", "updated_at": "2026-07-15 04:22:43.606430+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-products", "ai-tools"], "entities": ["GenAI Evaluation"], "alternates": {"html": "https://wpnews.pro/news/operationalising-multi-dimensional-evaluation-for-conversational-agents-a-with", "markdown": "https://wpnews.pro/news/operationalising-multi-dimensional-evaluation-for-conversational-agents-a-with.md", "text": "https://wpnews.pro/news/operationalising-multi-dimensional-evaluation-for-conversational-agents-a-with.txt", "jsonld": "https://wpnews.pro/news/operationalising-multi-dimensional-evaluation-for-conversational-agents-a-with.jsonld"}}