Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking
This paper presents GenAI Evaluation, a configuration-driven pipeline for large-scale evaluation of retail conversational systems. It processes production logs via normalization, sharding, asynchronous execution, and schema-constrained LLM scoring, evaluating helpfulness, truthfulness, clarity, tone alignment, and translation. Selective re-evaluation handles only invalid records; schema locking and versioned configs ensure auditability. The pipeline processes ~50,000 records daily and has evaluated over 2 million interactions. Validation on 12,980 human-labeled records achieved macro F1 0.93 and 89% translation accuracy.
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[Submitted on 13 Jul 2026]
Title:Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking
View a PDF of the paper titled Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking, by Niranjan Kumar M and 4 other authors
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Abstract: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.
Comments: 14 pages, 1 figure of design of architecture and 2 tables exploring the results and benchmarking
Subjects:
Artificial Intelligence (cs.AI)
MSC classes: 68T42(primary), 68T50, 68T35, 68T07(secondary)
ACM classes: H.3.1; I.2.7; I.2.4
Cite as: arXiv:2607.12085 [cs.AI]
(or arXiv:2607.12085v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.12085
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Niranjan M Mr. [view email] [v1] Mon, 13 Jul 2026 19:01:56 UTC (972 KB)
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